In order to solve the difficulty of feature extraction and low accuracy of retrieval in pulmonary nodule image retrieval， a deep network model named LMSCRnet was proposed to extract image features. Firstly， the feature fusion method of convolution of filters with different scales was adopted to solve the problem of difficulty in obtaining local features caused by different sizes of pulmonary nodules. Then， the SE-ReSNeXt block was introduced to obtain the semantic features with higher level and reduce network degradation. Finally， the high-level semantic feature representation of pulmonary nodule image was obtained. In order to meet the needs of massive data retrieval tasks in real life， the distance calculation and sorting process were deployed on the Spark distributed platform. The experimental results show that the feature extraction method based on LMSCRnet can better extract the image high-level semantic information， and can achieve 84.48% accuracy on the preprocessed dataset of lung nodules named LIDC， and has the retrieval precision higher than other retrieval methods. At the same time， using Spark distributed platform to complete similarity matching and sorting process enables the retrieval method to meet the requirements of massive data retrieval tasks.