In the process of extracting image contour features, interference from noise can lead to unclear dependency relationships between features, which affects the accuracy of feature information extraction results. Therefore, a laser image contour feature extraction method based on conditional generative adversarial networks is proposed. Firstly, the two-dimensional Otsu function is selected as the adaptability evaluation index of the bee colony algorithm, and optimization is carried out for the initialization population and bee search strategy; Then, using the sine and cosine method and the improved bee colony algorithm, the optimal segmentation threshold of the laser image is obtained by searching for the global optimal solution; Finally, in order to capture the global dependency relationship between features, residual structures and layered dilated convolution modules are integrated into the conditional generative adversarial network, combined with cross attention modules to ensure the smoothness of laser image contour lines. Meanwhile, by utilizing spectral normalization techniques and Leaky activation functions, the training process of the model is effectively stabilized, improving the comprehensiveness and accuracy of laser image contour feature extraction. The experimental results show that this method can obtain high-precision contour feature extraction results from laser images.