The purpose of this study is to improve the recognition rate and accuracy of fraud calls. We collect the communication process data such as users’ behavior of having telephone communications and surfing the Internet by a big-data platform and conduct a comprehensive analysis combined with users’ basic attributes and mobile terminal information; also, an identification model is built by the appropriate recognition algorithm for machine learning. The proposed method can better find the internal differences between fraud calls and ordinary ones. Compared with traditional analysis based on call behavior, it can effectively improve the identification accuracy and coverage of prank and fraud calls and reduce false negatives and false positives. The proposed method performs prominently better in fraud call identification, which can be used as a new technology choice, according to actual data verification.