User preference perception and intelligent search engine recommendation based on network behavior tracking with the web communities is analyzed in this research. In order to quickly and accurately detect the abnormal behavior of network users, it is first necessary to train and learn network data collected from the network that reflects the behavior of network users, to obtain user behavior characteristics, and to generate classification models. To this end, the selective integration learning technology is introduced into the collaborative learning process, and a selective collaborative learning method is proposed to obtain the user behavior classification model, so as to improve the training and learning effect while also adapting to the non-equilibrium training sample data. First, the actual network traffic data are collected and pre-processed, including the statistics and measurement of the traffic data according to the detection characteristic indicators to construct the network user behavior data. Some network user behavior data are marked by software tools, manual analysis and other methods to obtain the training samples of partial marks. Besides this, the web communities as the experimental data set has been analyzed to construct the efficient model. The experiment results compared with the latest methods have proven the effectiveness.