针对分析HPV-DNA病毒载量检测与TCT液基薄层细胞学技术联合检查中存在的不足，应用计算机辅助诊断(CAD)技术提高宫颈癌筛查准确率，提出了一种基于随机森林(RF)的宫颈病变识别模型。通过对宫颈细胞图片进行特征值提取形成宫颈细胞特征数据集，然后进行Bootstrap抽样，采用随机抽取一个特征子集建立决策树，对宫颈细胞分类结果进行投票，将此模型在宫颈癌筛查方面加以应用。经理论分析和实验数据验证，相比于传统的联合检查，该方法提升了宫颈病变识别的性能，其结果具有一定的竞争力、更具优势。

According to the analysis of the shortcomings of the joint inspection of HPV-DNA viral load testing and TCT liquid thin-layer cytology technical, this paper applies computer aided diagnosis (CAD) technology to improve cervical cancer screening accuracy, and proposes a cervical lesion recognition based on random forest (RF) model. Characteristic value of cervical cell image are extracted to form the cervical cells characteristic data sets, and then to do the Bootstrap sampling, using a random sample to build a decision tree. The results of cervical cell classification are voted out, and the model is applied to cervical cancer screening. According to theoretical analysis and experimental data, compared with traditional combined examination, this method improves the performance of cervical lesion identification, and its results have certain competitiveness and advantages.