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The conventional hospital information security protection mainly adopts the method of information attribute feature fusion classification for security protection, ignoring the security impact caused by the topological structure of information storage logical nodes, resulting in a high probability of successful security protection attacks. Therefore, this article conducted research on hospital access control systems. Propose using the improved variance feature method in machine learning to optimize the design of hospital access control systems. In the hardware design section, design the hospital access control terminal, control terminal and human-machine interface, and interface data transmission module; In the design of hospital access control schemes, the variance feature selection method is used to filter and normalize hospital information to complete data updates; Using 3DES encryption algorithm, establish a fuzzy judgment matrix for information, and complete the design of hospital information security access control system. After experimental testing, the system designed in this article can effectively reduce the success probability of various attacks, with an average attack success rate of only 2.33%. It has high security and effectively ensures the security of hospital information, avoiding leakage or failure of hospital information.