Abstract: Objective To explore the application of artificial neural networks in survival prediction for postoperative cervical cancer. MethodsClinical and pathological data of 125 cases of cervical cancer and treatment follow-up information, were collected and in accordance with the ratio of 4:1, randomly divided into a training group and test group, respectively. Through Logistics regression, significant factors were screened by univariate analysis to build the logistics regression model, and a probabilistic neural network (PNN) model was established by significant factors. The training group was trained by network and the test group was detected by network. Results The accuracy, sensitivity and specificity of PNN model was 92%,75% and 95.23%, respectively. The accuracy, sensitivity and specificity of logistics regression model was 84%, 50.0% and 82.61% respectively. Conclusion The neural network had a great deal of flexibility in the survival analysis. Nonlinear effects could be accommodated in the model, and random characteristics of the data such as the distribution was not required to make assumptions and might not meet the H0 supposition, The neural network had broad application prospects.