Objective To develop deep learning-based auxiliary diagnostic models for diverse pulmonary diffuse cystic diseases， and subsequently evaluate their classification performance to identify the optimal model for clinical diagnosis. Methods A total of 288 patients diagnosed with idiopathic pulmonary fibrosis （IPF）， pulmonary lymphangioleiomyomatosis （PLAM）， and pulmonary Langerhans cell histiocytosis （PLCH） were prospectively enrolled from the First Affiliated Hospital of Guangzhou Medical University between January 2010 and October 2022， comprising 76 cases of IPF， 179 cases of PLAM， and 33 cases of PLCH.A total of 877 CT cases were collected， comprising 232 cases of IPF， 557 cases of PLAM， and 88 cases of pulmonary PLCH. Based on the cutoff date of December 31， 2019， the CT scans were divided into two datasets： dataset A consisted of 500 CT scans including 185 IPF cases， 265 PLAM cases， and 50 PLCH cases； while dataset B comprised 377 CT scans with a distribution of 47 IPFcases， 292 PLAMcases， and 38 PLCH cases. The Dataset A was randomly partitioned into training set， validation set， and test set in a ratio of 7∶1∶2. Subsequently， six distinct deep learning neural networks were employed for training after preprocessing and data augmentation. Receiver operating characteristic curves were generated to assess the model performance using metrics such as area under the curve （AUC）， accuracy， sensitivity， specificity， and F1 score in order to identify the optimal model. Furthermore， a test set B comprising 30 randomly selected cases from dataset B for each disease type was utilized to evaluate the trained optimal model by employing the same aforementioned metrics. Results In test A， six well-established diagnostic models demonstrated superior classification performance for IPF and LAM， with an AUC greater than 0.9. For LCH， EfficientNet exhibited low classification efficiency with an AUC between 0.6 and 0.7， while Vgg11 showed an AUC between 0.8 and 0.9； the other four models displayed excellent classification efficiency with an AUC greater than 0.9. Except for Inception V3， the remaining five diagnostic models performed poorly in identifying and classifying LCH lesions. Considering multiple indicators， the InceptionV3 model showcased optimal comprehensive performance among the six models， achieving high evaluation parameters such as overall accuracy （94.90%）， precision （93.49%）， recall （90.84%）， and specificity （96.91%）. TestB was conducted using the trained InceptionV3 model resulting in an accuracy of 81%， precision of 82%， recall of 81%， and specificity of 90%. Conclusions Six recognition and classification models， developed using deep learning technology in conjunction with pulmonary CT images， demonstrate effective discrimination between LAM， LCH， and IPF. Notably， the model constructed utilizing the InceptionV3 neural network exhibits superior efficiency in accurately recognizing and classifying IPF and LAM.