A computer-generated image （CGI） detection network based on generalized center difference convolution （GCDC）and spatial layout mechanism（SLM）is proposed to meet the growing demand of computer-generated image detection technology in the fields of digital forensics and judicial expertise. Firstly，a correlation feature extraction module containing three parallel independent branches is designed. Next，the outputs of the three branches are concatenated and then fed into a channel attention mechanism submodule. Finally，five successive convolutional modules with spatial layout mechanism modules are utilized to learn higher level hierarchical representation for making decision. The detection accuracy on the SPL2018 and DSToK public datasets can reach 94.76% and 95.38%，which is 3.12% and 3.23% higher than the best comparison method in detecting generated images. The detection accuracy on the SPL2018 and DSToK public datasets can reach 94.76% and 95.38%，which is 3.12% and 3.23% higher than the best comparison method in detecting generated images. The ablation experiment verifies the contribution of each module in the network to the overall detection performance of the model. Finally，the robustness of the network to JPEG compression and additive noise is verified，and even for compressed images with a quality factor of 60，the detection accuracy can still reach over 84%.