An efficient name entity extraction based on part-of-speech (POS) tagging of term mining method was proposed. It would build a general term network is presented for entity relation visualization and exploration. Terms from each document in the corpus are first identified. They are subjected to the analysis for their association weights, which are accumulated over all the documents for each term pair. This study also modified the extraction based on POS tagging algorithm by studying literature approach. Numerous literatures were related to name entity extraction or POS tagging, there are only a limited amount of studies available on Chinese criminal intelligence analysis, which we believe is an easy yet powerful tool for crime investigation. This analysis scenario based on the collective terms of the similar type or from the same source enables criminal notes to show indirect relation network. Some practical instances of criminal intelligence analysis were demonstrated. Our application examples show that through this new methodology, more detail information and invisible relations in previous studies would be enhancing drawn out for visualization. Social network collects different kinds of information of people to form a semantic web, and plays an important role in the development and exploration of new information. From criminal investigation notes, Internet new, and litigation data, term network based on document co-occurrence, it would describe profiles of various clues. The contribution of this article is to present an efficient and effective term-correlation mining method by using name entity extraction of POS tagging. It would help law enforcement agent investigation and explore probable criminal acts more efficiently.