Abstract: The 3rd-order singular value decomposition recommendation algorithm can comprehensively consider the three parts of information of user, tag and item, and explore the potential relationship between the three to make recommendations. However, this method does not introduce any other effective information, such as the user's emotion. Considering more dimension information, in this paper we propose a 4th-order singular value decomposition recommendation algorithm based on the third-order one. The method extracts user's emotional preference from the emoji expression in the commentary, introduces a 4th-order tensor model to store user, user emotion, tag, and item quad data, and applies the 4th-order singular value decomposition to make personalized recommendations. The experimental results on an empirical dataset of online internet education shows that the proposed method has a significant improvement in accuracy and recall performance than the third-order singular value decomposition recommendation algorithm and the traditional recommendation algorithms. In the Top-1 recommendation, the accuracy rate and recall rate of proposed method can reach 0.513 and 0.339. The work of this paper provides a reference for personalized recommendation of mobile.