To improve the accuracy and robustness of modulation recognition, this study proposes an improved bimodal hybrid modulation recognition model. The model incorporates both the original time-domain in-phase and quadrature (I/Q) data, as well as amplitude and phase (A/P) format data to explore the spatiotemporal correlations within the signal. A two-branch symmetric structure is applied to further process the A/P data, enabling more effective learning of repetitive patterns while mitigating information redundancy. A bidirectional long short-term memory (BiLSTM) network is introduced to enhance the model’s capacity for complex temporal feature extraction. Experimental results demonstrate that the proposed model performs well on the RadioML2016.10A dataset. When the signal-to-noise ratio (SNR) is below –8 dB, the average recognition accuracy surpasses mainstream models by 6%. Within the SNR range of 0 to 18 dB, the average recognition accuracy improves by 2% to 10%, reaching 94.32% at 16 dB. In addition, when applied to the RadioML2016.10B dataset, the model continues to achieve superior performance, attaining a recognition accuracy of 93.91% at 18 dB.