To address the low accuracy of collaborative recognition caused by the difficulty of feature extraction in active and passive data collaborative recognition， this paper proposes an active and passive data collaborative recognition algorithm based on dual-channel convolution and an attention mechanism. The active and passive data are associated and fused， and then a dual-channel convolutional network is used to extract data features. On one channel， two large convolution kernels are employed to capture low-frequency features. Larger convolution kernels can enhance robustness to noise. On the other channel， small convolution kernels are used to enhance the neural network’s ability to extract detailed features. Meanwhile， an attention mechanism is used to enhance the network’s ability to extract key features， and a bidirectional LSTM network is added to extract complex temporal features. Experimental results show that the proposed method can effectively improve recognition accuracy and demonstrates strong practical applicability.