Accurate and robust pain intensity detection has significant implications for patient monitoring and rehabilitation, especially in personalized treatment and management. Benefiting from the complementarity of multiple modalities, multimodal fusion-based methods for pain intensity classification have garnered widespread attention. In this study, we propose an novel Bi-Modal Fusion framework based on Electrodermal Activity (EDA) and Electromyography (EMG) for pain classification. This framework combines LSTM with an attention module in a unified block to learn complex dynamic features from biosignals, effectively capturing both global and local patterns. Meanwhile, focal loss is used as the loss function to mitigate the impact of class imbalance during model training. Through extensive experiments, our method achieves an average accuracy improvement of 3.31% compared to SOTA across 11 sub-datasets of X-ITE pain database, with a notable improvement of 7.20% on the Reduced Electrical Tonic sub-dataset (RETD). Our research not only validates the effectiveness of the proposed method but also highlights its robustness across different modalities and sub-datasets. These findings lay a solid foundation for our long-term goal of developing an accurate and robust clinical multimodal AI monitoring system for pain detection and quantification.