Session-based recommendation algorithms only statically model a single preference of users and fail to capture the preference fluctuation of the users affected by the environment, thus reducing the recommendation accuracy. Therefore, this study proposes a session recommendation method that integrates dual-branch dynamic preferences. First, the heterogeneous hypergraph is used to model different types of information, and a dual-branch aggregation mechanism is designed to acquire and integrate the information in the heterogeneous hypergraph and learn the relationship between multiple types of nodes. Then, a price-embedded enhancer is used to strengthen the relationship between categories and prices. Second, a two-layer preference encoder is designed, which uses a multi-scale temporal Transformer to extract the user’s dynamic price preference, and a soft attention mechanism and reverse position encoding are used to learn the user’s dynamic interest preference. Finally, a gating mechanism is used to integrate the user’s multi-type dynamic preferences and make recommendations to users. By conducting experiments on two datasets, namely Cosmetics and Diginetica-buy, the results prove that there is a significant improvement in Precision and MRR evaluation metrics compared with other algorithms.