Addressing the issue of inadequate classification accuracy in named entity recognition via entity word-to-relationship modeling, we propose a method using rotational attention. Firstly, the text is encoded using the BERT model and Bi-LSTM, fol‐ lowed by extraction of features from input text using convolutional neural networks. Subsequently, the extracted feature sequence is inputted into the rotational attention model for output probability calculations, and the MLP layer is used for output classification. The study's outcomes affirm the efficacy and feasibility of the technique proposed in this paper, as it successfully yields superior re‐ sults on mainstream English databases including CADEC, GENIA and CoNLL2003, for named entity recognition exercise. 

Recognizing emotions through facial expressions has been widely applied in normal life. Among deep learning algo‐ rithms, Convolutional Neural Networks (CNN) have achieved great success in the field of facial expression recognition. However, it still faces the problem of information redundancy and data deviation, which affects the performance of facial expression recognition algorithm. Therefore, a Multi-scale Image Convolutional Network (GCN) based on landmarks extracted from facial images is pro‐ posed. The proposed method is simulated on CK+, JAFFE, FER2013 and RAF-DB datasets. The results show that the proposed method is superior to AUDN, BDBN, SCNN and other traditional deep learning frameworks, and has higher accuracy on different data sets.

In traditional teaching, due to differences in teachers, the knowledge connections between different courses are often over‐ looked, resulting in excessive repetition or failure to integrate new and old knowledge points. To make the knowledge points more smoothly connected in teaching, we help students to establish a knowledge connection system. Firstly, we organize a teaching salon where teachers fill out a teaching knowledge connection table to find fusion points between courses. Secondly, we use holographic projection technology to build a holographic projection pyramid. Finally, we use holographic projection during the teaching to help students recall previously learned knowledge and better integrate it with new knowledge, and understand new information. To sum up, holographic projection technology visualizes models in courses, deepens students' impressions, increases visual stimulation dur‐ ing learning, and enhances students' interest in learning. This innovative form of teaching tool and teaching method helps to deepen students' understanding and absorption of knowledge.