Summary: Current Pornographic images filtering algorithms have some problems, such as the high false positive rate toward the bikinis images and insufficiency of filtering pornographic images with pornographic actions. The paper proposes a novel pornographic image filtering model based on High-level Semantic Bag-Of-Visual-Words. Firstly, local feature points in sex scene are detected using the SURF algorithm and then high-level semantic dictionary is constructed by fusing the context of the visual vocabularies and spatial-related high-level semantic features of pornographic images. Experimental results show that the model has an accuracy up to 87.6\% when testing the multi-person pornographic images, which is significantly higher than the existing pornographic image filtering algorithm based on Bag-Of-Visual-Words.