Objective To explore the feasibility of the deep convolutional neural network (DCNN) judging the indications and prognosis of the total knee arthroplasty based on the trained DCNN computer learning system.Methods C1FAR-10 DCNN model based on TensorFlow (an open source system,Google,USA) optimized by Alex Krizhevsky were constructed.There were 400 cases with knee osteoarthritis from different databases used for analysis.Three hundred patients underwent total knee arthroplasty,while 100 did not.X-ray of 200 preoperative cases from the 400 cases and their enlarged image (50 times) were applied for training DCNN,while the enlarged images from other 200 cases were used to test the DCNN.The correlation and the regression between judgment of the DCNN and clinical truth were analyzed.The clinical truths were rechecked three times and were confirmed by treatment results.Pearson correlation and linear regression analysis were used.The relation test of the software was only used as a reference.Results There was no significant difference between the baseline of cases for learning and test.After learning 200 cases,the DCNN judged the 10 000 cases enlarged from remaining 200 cases.The correlation between the DCNN judgment and the clinical truth was not significant (r=0.000,F=0.001,P=0.970).False positive was observed in 1 681 cases,false negative in 3 296.After enlarged to 10 000 images,the correlation between the two judgments was significant (F=11 228.735,P=0.000,r=0.727 and R2=0.529).The software detection precision was 0.860.Conclusion DCNN can be applied in judging the indications of the total knee arthroplasty.Large sample size can improve the accuracy of the judgment significantly.