In order to improve the transmission stability of sensor networks, a sensitive data mining method based on Pan Boolean algebra is proposed. According to the output correctness, reliability and operation efficiency of wireless sensor network, this paper analyzes the characteristics of sensitive data, extracts and clusters the associated features of sensitive data, establishes the information clustering model of sensitive data in sensor network, and detects the fuzzy factor of sensitive data in sensor network with grid block clustering method, The Pan Boolean algebra analysis model is used to realize the hybrid deep learning of sensor network sensitive data detection and realize the optimization of sensor network sensitive data mining. The simulation results show that this method has high precision in mining sensitive data of WSN, and improves the reliability of WSN.

为了提高传感器网络的传输稳定性，提出了一种基于泛布尔代数的敏感数据挖掘方法。根据无线传感器网络输出的正确性、可靠性和运行效率，分析敏感数据的特征，提取敏感数据的关联特征并进行聚类，建立传感器网络中敏感数据的信息聚类模型，检测模糊敏感数据。针对传感器网络中敏感数据的因素，采用网格块聚类方法，利用泛布尔代数分析模型实现传感器网络敏感数据检测的混合深度学习，实现传感器网络敏感数据挖掘的优化。仿真结果表明，该方法对WSN敏感数据的挖掘精度较高，