随着旅游业的蓬勃发展和信息技术的迭代更新,旅游推荐系统成为辅助用户制定旅游规划的重要工具。传统的基于协同过滤推荐算法虽然能根据用户历史行为进行个性化推荐,但在数据稀疏性和非线性关系建模方面存在不足。提出一种基于深度学习的协同过滤优化算法,通过引入自编码器（Autoencoder）与深度神经网络（DNN）对用户和景点特征进行高维非线性映射,再结合传统协同过滤思想,实现对用户对旅游景点评分的精准预测。实验结果表明,该算法在精确率、召回率和F1值等指标上均优于传统算法,为智慧旅游领域的个性化服务提供了新的技术路径。

With the rapid development of tourism and continuous advancements in information technology, tourism recommendation systems have become crucial tools for assisting users in travel planning. Traditional collaborative filtering recommendation algorithms, while capable of providing personalized suggestions based on user historical behavior, exhibit limitations in addressing data sparsity and modeling nonlinear relationships. This paper proposes a deep learning-based collaborative filtering optimization algorithm. By introducing autoencoders and Deep Neural Networks (DNN) to perform high-dimensional nonlinear mapping of user and attraction features, combined with traditional collaborative filtering techniques, the method achieves accurate prediction of user ratings for tourist attractions. Experimental results demonstrate superior performance over conventional algorithms across metrics including precision, recall, and F1-score, offering a novel technical pathway for personalized services in smart tourism applications.