Medical and health field is a hot application field of wireless sensor networks. How to correctly refine and classify telemedicine sensor data is the research focus in related fields. Therefore, a detailed classification mathematical model simulation of telemedicine sensor data based on multi feature fusion is proposed. On the basis of telemedicine sensor data acquisition, it is preprocessed to reduce the computational overhead of detailed classification. The reliability features of the preprocessed telemedicine sensing data are extracted, the extracted features are fused by the principal component analysis method, and the refined classification model of telemedicine sensing data is constructed based on the principle of machine learning. The fused features are input into the model to complete the refined classification of telemedicine sensing data. The experimental results show that the correct refinement classification rate of the proposed method is more than 90%, the refinement classification accuracy is higher than 98.5%, the convergence speed is good, and the refinement classification time is 4 ~ 12 s, which proves that the correct refinement classification rate and accuracy of the proposed method are high, the classification time is short, and has good application performance.

医疗卫生领域是无线传感器网络的热门应用领域。如何正确提炼和分类远程医疗传感器数据是相关领域的研究热点。因此，提出了一种基于多特征融合的远程医疗传感器数据详细分类数学模型仿真。在远程医疗传感器数据采集的基础上，对其进行预处理，以减少详细分类的计算开销。提取预处理后的远程医疗传感数据的可靠性特征，利用主成分分析方法融合提取的特征，基于机器学习原理构建远程医疗传感数据的精细分类模型。融合后的特征输入到模型中，完成远程医疗传感数据的精细分类。