Abstract: Objective: To explore the application value of convolutional neural network (CNN) in CT diagnosis of skull base fractures. Methods: The skull CT image data of 3100 patients with skull base fractures and 2 467 normal patients was collected retrospectively. After the standard nanofiltration and actual model calculation, the skull base CT image data of 2 488 patients with skull base fractures and 1 628 normal patients were selected. The CT images were labeled and randomly assigned into training set and test set. The skull area discrimination algorithm model and skull base fractures detection algorithm model were established by CNN, then we performed verification on the models through skull base area discrimination, skull fractures and skull base fractures in the test. The detection indexes included precision, recall and average diagnosis time consumption. We carried out comparisons of diagnostic efficacy with the artificial group (junior radiologist) test. Results: We carried out test comparisons on the steady models obtained by CNN algorithm, the results showed that the accuracy of the whole skull base fractures (including the anterior, middle and posterior skull base fractures) was less than 0.5, which was lower than that of the artificial group (all higher than 0.63); The recall rate > 0.89 was better than that of the artificial group (all < 0.8); The average diagnosis time was (3.12±67)s, significantly less than that of artificial group. In the area test of skull base fractures, the accuracy rate was anterior skull base > middle skull base > posterior skull base while the recall rate was middle skull base > posterior skull base > anterior skull base. Conclusion: The algorithm model of skull base fractures based on CNN is superior to the artificial test results in recall rate and diagnosis time consumption for CT diagnosis of skull base fractures in patients with craniocerebral trauma, which has certain value in assisting clinical diagnosis, reducing missed diagnosis and diagnosis time consumption.