Aiming at the two key points (feature extraction and fault recognition) of bearing fault diagnosis, a new car motor bearing fault diagnosis method was proposed. At the feature extraction link: a feature extraction method based on LCD decomposition and symbol entropy was proposed. At the fault identification link: in order to improve search ability of fruit fly optimization algorithm (FOA) to relevance vector machine (RVM), study of “history” strategy was introduced to FOA, then, FOA with history study ability (HSAFOA) was proposed and effectively improved the classification performance of RVM. Different fault types and different fault degrees of rolling bearing fault diagnosis experiment results show that the LCD symbol entropy can represent fault effectively and HSAFOA improved the identification accuracy of RVM, it has a certain superiority when compared with some other methods.