In order to effectively evaluate personnel performance, a distributed data mining algorithm for spatial networks based on BP neural wireless network is proposed. In the cloud computing environment, an excavator is used to construct multiple input multiple output spatial network data, analyze the data structure, and perform redundant data compression of massive data through time-frequency feature extraction. Combined with the adaptive matching filtering method, the characteristics of the data are matched. The spatial frequency feature extraction method is used to locate the features of the multiple-input multiple-output spatial network data. In order to improve the accuracy of data mining, the BP neural network is used to classify and identify the extracted data features to achieve the optimization of data mining. A wireless sensor network is a wireless network composed of a large number of stationary or moving sensors in a self-organizing and multi-hop manner. It cooperatively senses, collects, processes, and transmits the information of the perceived objects in the geographical area covered by the network and finally puts these The information is sent to the owner of the network. This algorithm improves the accuracy of personnel performance evaluation, simultaneously establishes a hierarchical analysis and quantitative evaluation model for the performance of government managers, and adjusts the results of hierarchical statistical analysis on government administrators as needed. The performance evaluation and optimization of government administrators were introduced. The empirical analysis results show that the method has higher accuracy for government managers’ performance evaluation, higher efficiency of big data processing, and better integration.

为了有效评价人员绩效，提出了一种基于BP神经网络的分布式空间网络数据挖掘算法。在云计算环境中，挖掘机用于构造多输入多输出空间网络数据，分析数据结构并通过时频特征提取对海量数据进行冗余数据压缩。结合自适应匹配滤波方法，可以对数据的特征进行匹配。空间频率特征提取方法用于定位多输入多输出空间网络数据的特征。为了提高数据挖掘的准确性，使用BP神经网络对提取的数据特征进行分类和识别，以实现数据挖掘的优化。无线传感器网络是由大量固定或移动传感器以自组织和多跳方式组成的无线网络。它协同感测，收集，处理和传输网络覆盖的地理区域中被感知对象的信息，最后将这些信息放入网络中。该算法提高了人员绩效评估的准确性，同时建立了政府管理人员绩效的层次分析和定量评估模型，并根据需要调整了政府管理人员的层次统计分析结果。介绍了政府行政管理人员的绩效评估和优化。