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Because of their portability, electric motorcycles are usually pushed into elevators by residents and charged in the home, which has serious safety risks. Traditional manual-based methods to manage this behavior have poor monitoring effects and high costs. As for automatic management systems using artificial intelligence (AI), the deployment method matters. Cloud-based deployment methods have the disadvantages of high latency, high risk of privacy leakage, and heavy network transmission loads. In this paper, we propose a highly secure edge-intelligent electric motorcycle management system for elevators. By using edge-based deployment method, the monitor pictures are processed locally without being uploaded to the cloud, which can effectively resist network attacks and prevent residents' private data from being leaked. To improve the system security, we fully analyze the challenges faced in the application scenarios and introduce security threat identification (STI-1H8) model to identify the security threats. In addition, we propose several data enhancement methods to improve the system recognition accuracy. Experimental results show that our system can achieve a high recall rate of 0.82. By using data enhancement and data mixing strategies, it can reduce the misjudgment rate by 0.35. Moreover, compared to cloud computing, our edge-based method can reduce the latency by 19.6%, meeting real-time requirements.