The complex environment of wireless laser communication poses a serious challenge to the reliability of wireless laser communication signals. Therefore, a method for detecting abnormal wireless laser communication signals under multiplicative noise interference is proposed to take timely processing measures and ensure the quality of wireless laser communication. Integrating convolutional neural networks and machine learning methods, a wireless laser communication signal anomaly recognition model based on deep learning technology is constructed to determine the existence of anomalies. Based on the identified abnormal characteristics of wireless laser communication signals, estimate abnormal parameters such as center frequency point, pulse period, and scanning rate to complete wireless laser communication signal anomaly detection. The experimental results show that the normalized recognition indices for different signal anomalies are as high as 0.98, 1, 1, 0.99, and 0.99, and the normalized root mean square error is low, which proves that the proposed method has high detection accuracy and superior detection performance.