Telephone fraud is increasingly rampant, affecting the safety of people’s lives and property seriously. How to effectively prevent telephone fraud has become a great concern of society. This paper proposes a fraud phone recognition method based on the Attention-BiLSTM model. This method uses phone text as a data set and adopts a bi-directional long short-term memory (BiLSTM) model to extract the long-distance characteristics of a sentence. Furthermore, the attention mechanism is utilized to enhance the meaning feature weight of the words related to the fraud parts in phone text. The feature vector representation of phone text at the sentence level is achieved and inputted to the Softmax layer for classification prediction. The experimental results show that the BiLSTM fraud phone classification model based on the attention mechanism has the accuracy increased by 2.15% and 0.6% respectively compared with baseline models, possessing more excellent prediction performance.