Abstract: Objective: To construct an improved deep learning computer-aided diagnosis model based on convolutional neural network (CNN) and Attention Mechanism proposed as Inception Spatial and Channel Attention Network (ISANET) and evaluate the model's performance, comparing it with the traditional CNN model. Methods: A total of 619 lung CT images of 60 patients with lung squamous cell carcinoma or lung adenocarcinoma confirmed by surgical pathology and 30 patients with normal lungs were collected retrospectively to form Dataset A; a total of 737 public dataset images were collected to form Dataset B. The two datasets were randomly divided into training and test sets at a 6:4 ratio. Construct the ISANET model and conduct training and verification, then record the precision ratio and recall ratio to calculate the F1 score and evaluate the performance of the ISANAT model. Finally, the ISANET model was compared with the traditional CNN models such as AlexNet, VGG16, InceptionV3, MobilenetV2, and ResNet18 by drawing the Precision-Recall (P-R) curve and calculating the area under the P-R curve to evaluate the classification performance of different models for LUSC and LUAD. Results: Compared with the traditional CNN model, the accuracy of the ISANET model for non-small cell lung cancer classification improved significantly, reaching 99.6% and 95.2% in Dataset A and Dataset B, respectively. Conclusions: The ISANET model provides better non-invasive prediction of LUSC and LUAD, improves the accuracy of CT imaging identification of lung squamous cell carcinoma and lung adenocarcinoma, and can help diagnosticians quickly and accurately classify non-small cell lung cancer.