Session-based recommendation has demonstrated notable advantages, since it dynamically adjusts recommendations based on user’s behavior and context within the current session. Recently, researchers have extensively investigated the integration of knowledge graphs (KGs) into recommendation systems, with Graph Neural Network (GNN) achieving impressive results in deeply exploring and learning the relationships between users. However, despite these encouraging results, previous studies have overlooked the semantic information of items within sessions and the contextual environment outside sessions, making it challenging to mine the complex associations between items. Simultaneously, traditional algorithms encounter issues with high computational costs and excessive memory usage. To address the above challenges, in this paper, we propose a KG-enhanced session-based recommendation with two-stage feature filtering, named KGFF-SR. We introduce KG as an external knowledge base and utilize the link information between entities in the graph to enrich the session graph. Furthermore, we propose a two-stage item feature filtering mechanism to capture potential relationships between users and items while reducing computational and storage costs effectively. We conduct extensive experiments on three real-world public sub-datasets, and the results show that our proposed method significantly outperforms powerful baseline models in performance.