This paper used user behavior in Web 2.0 as a research object, explored ways of user feedback, and proposed the concept of user feedback score. It studied the specific methods and corresponding realization for user feedback impacting the final ranking of search results, and presented a sorting algorithm for search results based on neural network. The algorithm used the BP neural network model, select samples to train the neural network based on user feedback score. Traditional search results will be put into the trained neural network to compute, and a new ranking will be made according to relevance of the web page which indicated by the calculated results. This algorithm used the neural network’s pattern recognition capabilities, combined user feedback and search engine effectively, making search results more in line with the user，s search request.