In the paper, our research of abnormal bus data analysis of intelligent and connected vehicle aims to detect the abnormal data rapidly and accurately generated by the hackers who send malicious commands to attack the intelligent and connected vehicles through three patterns, including remote non-contact, short-range non-contact and contact. Because the vehicle bus is the final connector to the electronic control unit (ECU), we can monitor all threats through detecting the abnormal data on bus. Based on that, drivers or vehicles can respond to the cybersecurity problems in time, adopt corresponding security mechanism and avoid serious losses. The research routine is as follows: Take the bus data of 10 different brands of intelligent and connected vehicles through the real vehicles experiments as the research foundation, set up the optimized neural network, collect 1000 sets of the normal bus data of 15 kinds of scenes and the other 300 groups covering the abnormal bus data generated by attacking the three systems which are most common in the intelligent and connected vehicles as the training set. In the end after repeated amendments, with 0.5 seconds per detection, the intrusion detection system has been attained in which for the controlling system the abnormal bus data is detected at the accuracy rate of 96% and the normal data is detected at the accuracy rate of 90%, for the body system the abnormal one is 85% and the normal one is 80%, for the entertainment system the abnormal one is 80% and the normal one is 65%.