At present， how to capture the dynamic changes and dependencies of user behaviors is an important problem in the field of sequential recommendation， which mainly faces challenges such as large behavior event space and complex sequential dependencies of behaviors. To address the above challenges， a sequential recommendation algorithm based on the learning of latent states of behavioral sequences and their dependency relationships was proposed. Firstly， the low-dimensional representation of the latent states of behavioral sequences was obtained by using the maximum pooling hierarchical structure. Then， the dependencies between the latent states were captured and described by graph neural network in order to achieve the learning of user behavior change patterns， which led to more accurate sequential recommendation effect. Experimental results show that compared with the recent Hierarchical Gating Network （HGN） baseline algorithm on the IPTV， New York City （NYC） and Tokyo （TKY） datasets， the proposed algorithm improves the performance evaluation metric recall by 30.03%， 29.48% and 33.75% respectively， and obtains 37.20%， 43.47% and 40.34% relative improvements on Normalized Discounted Cumulative Gain （NDCG） metric， respectively. And the ablation experimental results demonstrate the effectiveness of dependency learning of sequential states. Therefore， the proposed algorithm is especially suitable for solving the problems with sparse behaviors in single time slice and complex behavioral dependencies in sequential recommendation.