With the rapid development and wide application of artificial intelligence, intelligent investigation is becoming a new research hotspot in forensic science, and the realization of automatic recognition and classification of crime scene photographs is an essential aspect of intelligent investigations. We present an algorithm that automatically classifies crime scene photographs based on a convolution neural network. First, based on the data from criminal cases, a crime scene photograph dataset was constructed comprising 13164 scene photographs and 4008 negative photographs. Second, crime scene photograph net (CriSNet) was designed based on the data characteristics to accurately classify crime scene photographs by adding normalization processing to the convolution layer and improving the bottleneck module. The experimental results show that the accuracy of CriSNet is 1 percentage point better than that of the benchmark with good robustness, and CriSNet can still maintain excellent performance under low resolution and poor-quality conditions.