Abstract: Aiming at the problem of mining potential behavior rules of enterprise employees under a large amount of heterogeneous data, a behavior analysis method based on mean clustering is proposed. Based on the behavior data of employees in a scientific research institute, a behavior analysis model is established to extract and select behavior characteristics from the access control card data of enterprise employees and professional daily office software data, and the behavior characteristics are analyzed by K-Means cluster analysis. Finally, in terms of work attitude, employees can be roughly divided into diligent, sloppy and ordinary. In terms of job characteristics, employees can be roughly divided into ordinary, professional and management categories. And through the analysis of the clustering results, some hidden behavioral characteristics of the employees are excavated. Through the investigation of relevant personnel on site, combined with the real work nature and position characteristics of employees, it is verified that the data generated by the application of employee behavior in this scenario, combined with the clustering algorithm, can achieve ideal results in the analysis of enterprise employee behavior.

Key words: employee behavior analysis, cluster analysis, behavior feature extraction