To filter the variety of pornographic images in the reality Internet, the study proposed a Pornographic Images Recognition (PIR) framework based on multi-classification and deep Residual Network (ResNet). Traditional methods usually consider the PIR task as a binary classification, while the approach presented in this paper divides porno images into 7 detailed classes based on its variety features with 2 more benign image classes (with or without human in it). The approach relies on 50-ResNet to extract image features automatically, and then decides whether it belongs to porno images based on the highest score and gives threshold value. At training stage, a feedback-reconstruct training tactics is adopted for the network to collect better features. To deal with images in different scales, a monolateral sliding window method is taken to get better performance. After testing on the data set constructed with collected images from the Internet, the experimental result shows that the approach can reach high accuracy with lower time cost.