In response to challenges such as relation overlap complexity, semantic understanding difficulty, and class imbalance in relation extraction, this work introduces the adaptive feature fusion enhanced cascade pointer network (AFFCPN). The method leverages the Chinese pre-trained language model RoBERTa (Robustly Optimized Bidirectional Encoder Representation from Transformers) to enhance deep semantic understanding in Chinese text with lengthy and intricate structures. It also utilizes entity boundary information from the named entity recognition model to precisely identify entities. By employing the adaptive feature fusion module, it extracts crucial features from the start and end positions of subject entities, effectively utilizing key information to improve judgment regarding object entity boundaries. Moreover, a cascade pointer network structure is adopted to manage overlapping relation structures efficiently, thereby enhancing the model’s capability to extract complex overlapping relations. Additionally, the model incorporates the Focal Loss function to enhance extraction performance on minority class relations by adjusting class weights more directly and effectively. Experimental results across three diverse datasets showcase that the model’s performance surpasses that of existing mainstream relation extraction models. This not only validates its effectiveness in handling overlapping and imbalanced relations, but also demonstrates its applicability in general domain relation extraction tasks.