Abstract: The present disclosure provides a method and an apparatus for identifying a dark web website in a scenario where a plurality of pages are accessed simultaneously. The method includes: obtaining browsed network traffic packets of websites to be identified, and extracting direction sequence features from the network traffic packets; dividing the direction sequence features into a plurality of subsequence features based on a plurality of sliding windows, and inputting the plurality of subsequence features into a neural network model to extract preset pattern features; analyzing a correlation of the preset pattern features using a target website identification model to obtain a probability result of a target website being accessed; obtaining a target website identification result in the website to be identified based on the probability results and a preset classification model.