Due to rarely considering document popularity and personalization issues at the same time in the extraction of semantic features based semantic matching algorithms, the accuracy is low in the field of information retrieval. To solve the problem, an improved personalized information retrieval algorithm DSMN is proposed. DSMN bases on the deep struct semantic model (DSSM), uses independent recurrent neural network (IndRNN) to extract semantic features, and process long sequences. In DSMN, the self-attention mechanism is used to further extract the features, and the semantic similarity is calculated. By combining the semantic similarity with the processed user characteristics and document popularity, the relevance score of the query and the document is calculated to improve the efficiency of information retrieval. Experiments are done on four datasets, and the results show that the performance of DSMN is significantly better than the other state-of-the-art information retrieval algorithms based on semantic matching.