目前复杂间质性肺疾病存在分类精度不高，且缺少辅助诊断信息的问题，针对这些问题提出了基于多特征融合和有监督对比学习方法的图像检索框架。使用Res-Net50和影像组学特征提取模块提取间质性肺疾病特征。为了使不同模态不同尺度的两个特征进行融合，设计了一个可以通过两个特征联合表征空间计算特征相关性的特征融合模块。通过有监督对比学习方法，提升间质性肺疾病类别之间的特征区分度，并对典型间质性肺疾病数据库进行检索。在本次间质性肺疾病数据的检索任务中获得了最高的精确率、召回率和F₁分数，在用于图像检索的特征向量区分度指标中，获得了0.482的轮廓系数。实验结果表明：与传统深度学习单一特征模态方法相比，所提方法能有效提高间质性肺疾病图像分类检索精度，并提高间质性肺疾病诊断的可解释性。

At present, complex interstitial lung diseases have the problems of low classification accuracy and lack of auxiliary diagnostic information. To address these problems, an image retrieval framework based on multi-feature fusion and supervised contrastive learning methods was proposed. Interstitial lung disease features were extracted using Res-Net50 and radiomics feature extraction modules. In order to fuse two features of different modalities and scales, a feature fusion module was designed that can jointly represent the spatial calculation feature correlation of two features. The feature discrimination between interstitial lung disease categories was improved through supervised contrastive learning methods, and a typical interstitial lung disease database was retrieved. The highest precision, recall rate and F₁ score were obtained in the retrieval task of interstitial lung disease data, and a silhouette coefficient of 0.482 was obtained in the feature vector discrimination index for image retrieval. The experimental results show that compared with the traditional deep learning single feature modality method, the proposed method can effectively improve the classification retrieval accuracy of interstitial lung disease images and improve the interpretability of interstitial lung disease diagnosis.