Aiming at the problem that existing models cannot adequately fully consider the latent intent in user interaction sequences and the overfit of models, the Stochastic Shared Embeddings and Latent Intent Aware Self-Attention for Sequential Recommendation (SSELISR) are proposed. Temporal convolutional networks are used to convolve the sequence of user interactions deeply, for obtaining a representation of the user’s latent intent for the project. The absolute position and time interval of the project is modeled by using the time interval perception self-attention layer. The output of temporal convolution layer and time-aware self-attention layer is used as the intention time-aware attention layer to predict the query, key and value of the next item and find the correlation of items with latent intent. Stochastic shared embedding techniques are used to reduce model overfitting caused by over-parameterization and improve model recommendation accuracy through randomly transformations between embeddings. The experimental results show the SSELISR model, on the microblog and movie datasets NDCG@10 and Hit@10. Compared with the baseline models of GRU4Rec+, Caser, MaRank, and TiSASRec, the two aspects improved by 2.21%, 0.871%, 1.43%, and 3.99%, respectively. Ablation experiments also verified the interpretability and effectiveness of random shared embedding an underlying intent modules. Code is available at: Link Text