Aiming at the problem of ineffective emotion recognition in virtual sports, this study proposes a multimodal fusion emotion recognition algorithm based on facial images and heart rate variability (MFER-FIHRV). By capturing and analyzing data from both facial image and heart rate variability (HRV) modalities, the proposed approach enables a fine-grained perception of users’ emotional changes, thereby facilitating a personalized human-computer interaction (HCI) experience tailored to their current emotional state. The algorithm first employs a multimodal fusion Transformer to achieve complementary learning between facial images and HRV data. Then, a multimodal feature fusion strategy is utilized to concatenate fused features with the original feature representations. Additionally, a lightweight self-attention mechanism is introduced to capture high-level semantic representations within the multimodal data. Extensive experiments were conducted on two public datasets, demonstrating the superior performance of the proposed method. The results confirm its effectiveness and robustness, providing valuable insights for the design and development of user experience systems.