## 1 Introduction  
With the popularization of the Internet and the advent of the information age, the information that Internet users face and process every day is increasing exponentially. In order to select the content of interest, the recommendation system emerges as the times require. Most previous research work is based on uninterrupted user history for personalized recommendations, but in many services, such as YouTube and TikTok, due to limited storage resources, it is not possible or unnecessarily to track the user identity behavior. In other words, user profiles and long-term historical interactions are not available; only short-term session data from anonymous users are provided. For this scenario, research on session-based recommendation algorithms becomes more and more important [1]. The behavior information of user clicks generated within a certain time interval is a session sequence. In order to process this behavior information, the session-based recommendation system extracts the dependencies of multiple click items in the sequence and captures the importance of sequence prediction [2]. The interests of users in a certain period of time are the same and similar, which is important for the recommendation field, so the recommendation algorithms based on session sequence have received more attention in recent years [3]. At present, many session-based recommendation models have been born. In the early days, the session recommendation of Markov chains was mainly based on the user's previous behavior to predict the user's next behavior. With the development of deep learning, recurrent neural network (RNN) methods are gradually applied to session recommendation; such methods input user session click sequences into RNN to generate recommendation results. RNNs are effective to some extent, but most existing RNN-based models have limited representational power in capturing complex transitions between items. Recently, recommendation algorithms based on graph neural networks (GNN) have also become popular. [PERSON] et al. [4] adopted the GNN method to recommend users, converting session data into a graph structure and combining multiple layer gated recurrent units (GRU) to capture users' long-term interests and current preferences. This type of method inputs the session click item matrices constructed by the session graph and the session click sequence into the GNN for sequence modeling, which solves the problem of weak inter-session dependencies, and its recommendation effect is greatly improved, but it is not well filtered against useless dependencies. With the increasing popularity of deep neural networks, attention networks have been widely developed in many fields (e.g., computer vision, natural language processing, and recommendation systems), using self-attention mechanisms derived from the field of natural language processing to process session algorithms for sequences that are also emerging. In order to overcome the above limitations, this paper combines graph neural networks (GNN for brevity), self-attention networks (SAN for brevity), soft attention mechanisms, and other related technologies to propose a session-enhanced graph neural network recommendation model (denoted as SE-GNNRM). This model includes an item embedding layer, a session embedding layer, and a prediction layer. The item embedding layer includes two modules, GNN and SAN. The item embedding obtained by GNN and SAN is combined through a joint function. In the session embedding layer, the output item embedding of the item embedding layer is combined with the location information, and then the session embedding is learned using attention. The session embedding at this time only represents a single session embedding, and similar sessions may have similar preferences. To solve this problem, this model designs a dynamic global session interaction layer for learning similar session embeddings.