Chinese landscape painting mainly describes the natural landscape of mountains and water. It is an important branch of Chinese paintings. Currently deep learning models achieve significant success in many domains, such as image classification, object recognition, image style transformation and automated image generation. In this research, a Chinese landscape painting automated generation model based on generative adversarial networks (GAN) is proposed. The model is trained with Chinese landscape painting images from Internet. The depth of the network and loss function are properly designed. The generator and discriminator are trained in an adversarial manner and finally a well-trained generator is obtained. Compared with true Chinese landscape paintings, the proposed model can generate images with Chinese landscape painting style.

The construction of characteristic specialty groups in higher vocational colleges aims to improve the linkage of related spe- cialties and the weak regional development and innovation of industrial structure by introducing the knowledge system of different industrial structures into the setting and improvement of professional courses. Focusing on the construction of characteristic special- ty groups in higher vocational colleges, this paper makes an analysis and discussion on the development direction of the advanta- geous disciplines and characteristic specialty courses in different higher vocational colleges, formulates a clear talent training pro- gram of professional clustering, optimizes the resource allocation, promotes the coordination of the resources of school and enter- prise characteristic specialty groups, and improves the quality of professional talent training.

Online learning has gradually become an important form of learning in higher vocational colleges, and this field has be- come one of the current research hotspots. In this paper, online learning is briefly introduced, and the application of big data technol- ogy in online learning platform is briefly analyzed, focusing on the research and analysis of online learning behavior elements of five-year higher vocational nursing students, so as to provide reference for better online learning evaluation, to promote better online learning in higher vocational colleges and improve the quality of online learning.

Based on MOOC and big data analysis technology, this paper constructs the intelligent teaching mode of "MOOC + dynamic learning data analysis" . Firstly, the paper introduces the construction of intelligent teaching environment; then, accord- ing to the teaching environment, it expounds the teaching design of intelligent teaching mode in three stages: before class, in class and after class, and gives the steps and specific process of each stage in detail; finally, the significance of this study is summarized.

With the national attention to the innovation and entrepreneurship education of college students, it is of great significance for vocational colleges to actively explore the construction of entrepreneurial technological communities in combination with their own characteristics of application technology orientation. Taking the project development community in software technology major as an example, this paper mainly studies on the construction strategies of entrepreneurial technological community in higher voca- tional education, including "entrepreneurial company + studio" operation mode, "point to area" student community learning mode, "two-way complementary" mode of community and teaching construction, "four-dimensional" school support mode, and other effec- tive regular activities of community. By analyzing the application effect of this strategy in practice, it can be determined that this pro- gram has a very good auxiliary role in improving students' professional skills, promoting students' entrepreneurial ability and the growth of majors in the school.

The characterization ability of neural networks provides a convenient tool for remote-sensing image object detection tasks. However, the current high computational cost of mainstream neural network models has limited their application to real-time object detection tasks in remote-sensing images. This paper presents a lightweight neural network model for real-time object detection in re- mote-sensing images. The experimental results show that the method proposed in this paper keeps the detection accuracy equivalent to Yolov3, the model size is about one fifteen of Yolov3, and the network model can achieve a better balance in object detection accu- racy and computational overhead.

The Internet of Things industry needs a large number of composite and innovative technical skills. This article discusses on the collaborative construction plan of the professional group centered on the application technology specialty of the Internet of Things. It integrates the Internet of Things application technology, applied electronics technology, big data technology and application, and software technology with the Internet of Things industry chain. It supports each other to jointly build the complex and innovative technical talents required for the IoT industry chain.

Robot is one of the top ten key development fields of“Made in China 2025”, in order to meet the needs of social and eco- nomic development, higher vocational colleges should seize the opportunity and vigorously cultivate professional talents in the robot industry. Through the in-depth investigation of the robot industry enterprises in the Pearl River Delta region, the project team analyz- es and concludes that the robot installation and debugging, maintenance, product sales and after-sales service posts demand the most. Based on the job tasks and ability requirements of professional posts, this paper clarifies the training objectives and training specifi- cations of robot professionals in Higher Vocational Education with the integration of industry and education, and constructs the pro- fessional curriculum system of the integration of industry and education of“post orientation and practice subject”.

At present, the experimental teaching content of analog electronic technology is not perfect, the experimental teaching pro- cess is too programmed, and the experimental teaching system is not perfect. The teaching team of the course of analog electronic technology in our university carries out the experimental teaching reform of analog electronic technology. A set of experimental teaching mode consists of circuit design and simulation, bread board experimental circuit test, universal board welding test, PCB plate making and improvement. The practice proves that the reformed teaching mode and the examination and evaluation system are more helpful to improve the students' practical ability, cultivate their comprehensive innovation ability, and stimulate their initiative and enthusiasm.

With the development of 5G business, higher requirements are put forward for local transport and bearing network. The deployment of mobile bearing network based on SPN technology becomes an important means to solve the problem. This paper introduces the main bearing technology and network structure of 4G service local transmission network, as well as the difficulties faced by the existing transmission bearing network for carrying service of 5G, puts forward the application strategy of the transformation and construction scheme of 5G-oriented local transmission network, and gives a case of SPN network deployment in a city of Shandong Province.

With the approaching of 5G network commercial steps, the 5G network planning method of high-speed rail scene is becoming a subject that needs further study. This paper analyzes the wireless scene of high-speed rail, points out the existing problems,puts forward the planning ideas and methods of highspeed rail 5G network, and the methods are verified by system simulation.

With the continuous improvement of national economic construction, the living standard of Chinese residents has been rising steadily. The application of information technology has penetrated into every aspect of people's production and life, and the amount of data production has grown exponentially, marking the advent of the era of big data. In the era of big data, computer network bears the important responsibility of collecting, storing and transmitting information. However, with the increasing dependence of Chinese residents on computers and the Internet, problems also follow. Nowadays, network security problems are frequent and information leakage is severe. Almost everyone has experienced the experience of personal information being leaked online and suffered losses to a greater or lesser extent. This paper analyzes the source of computer network security problems in the era of big data and puts forward effective preventive measures.

Radar signal processing is one of the core contents of modern radar systems, which directly affects the scope and perfor- mance of radar systems. The effective identification of radar signals is an important part of predicting unknown radar signals. This paper uses the support vector machine (SVM) to intelligently identify four different radar signals, and selects the radial basis func- tion (RBF) as the support vector’s non-linear mapping function. The theoretical derivation shows that the penalty factor c and the kernel function parameter g affect its classification performance. This paper uses particle swarm optimization (PSO) to optimize two important parameters of support vector machines. The results show that the classification performance of SVM without parameter optimization is unstable and the recognition accuracy is between 79.6992% and 90.2256%, while the accuracy of SVM optimized by particle swarm optimization algorithm is as high as 100%. The effectiveness of the optimization method is effectively proved, and the accurate recognition of radar signals of SVM based on particle swarm optimization algorithm is realized

With the extension of running a college, the original method of on-site manual repair based on the department of Teacher- student Service Center has been unable to meet the growing demand for logistics repair and maintenance. According to the actual sit- uation of the school and the analysis on the logistics management system, this paper draws the following conclusions: the logistics management system of the vocational college should meet the functional requirements of online repair application, online view, maintenance feedback, online evaluation, etc. It designs the logistics repair application system from the system architecture design, network architecture design, UML class diagram design, database design, etc. The development of the logistics repair system is real- ized by C# programming.

This paper focuses on the design of a remote diagnosis system based on cloud computing, big data and internet of vehicles technology, and describes the technical architecture, module function composition and key technologies of the system in detail. Sec- ondly, combined with the current situation of domestic automobile enterprises, this system is applied to solve some problems of pain points in automobile R & D, trial production certification, factory production, after-sales maintenance and so on.

The increasing amount of data in the database makes it time-consuming and laborious for users to query using the database system. The traditional query optimization method has been unable to meet the requirements of today's data query, thus improving the efficiency of database system optimization has become a hot spot of research. In this paper, the distributed query processing tech- nology based on the semi join algorithm is proposed to optimize the database system. This paper selects the semi join algorithm to optimize the algorithm design, proposes the semi join operation query optimization algorithm (SDD-1), and verifies it by experiment to calculate the cost of the query algorithm. The results show that the query optimization strategy can significantly reduce the trans- mission cost and improve the overall efficiency of query.

In order to improve the employment percent of students in higher vocational colleges, it is necessary to predict their em- ployment situation and find out the main factors that affect their employment. This paper uses the cluster analysis intelligent technol- ogy of data mining technology to analyze the data of graduates in the database of higher vocational colleges, and forecasts the em- ployment situation in the future through the analysis results. By using the model-based method of clustering technology, the main factors affecting employment can be found through the established classification tree. The relevant leaders of employment manage- ment in higher vocational colleges can make decisions according to the classification results, which is conducive to improving the employment percent of higher vocational colleges.

In the research of channel decoding combined with deep learning technology, the problem of dimension limitation has al- ways been the focus of researchers. Since the deep neural network is storage intensive, the channel decoder of deep neural network usually needs much more computation consumption and memory than the conventional belief-propagation (BP) decoding. In order to alleviate this problem, an improved neural network decoder for LDPC code is proposed. According to the weight parameter value distribution in the deep neural network channel decoder, weight parameters are selectively added to a new neural network decoder. By limiting the number of training parameters, the scale of the deep neural network channel decoder is reduced. And our algorithm gets a large decoding gain than BP decoding.

Critical thinking is one of the ways of thinking to solve problems advocated by modern higher education. In the teaching process, the combination of critical thinking and university curriculum content can promote the reform of teaching methods. The cul- tivation of critical thinking requires teachers to guide students to learn knowledge with problems in the actual teaching process, and explore the nature of problems through discussion and practice, so as to form good thinking habits and cultivate students' innovation ability. This research will focus on the cultivation of students' critical thinking mode based on the reality and the relevant contents of C programming, hoping to promote the progress of teaching reform.

At present, virtual reality technology has been widely used in the field of education. The construction and application of ho- lographic teaching resources based on VR technology have attracted the attention of scholars. How to use this hot technology well is a hot topic in the field of education. This paper firstly describes the current situation and the main role of holographic teaching re- sources; then introduces the application of holographic teaching in typical professional teaching; and finally puts forward reasonable suggestions for the construction and development of holographic teaching resources.