With the development of wireless capsule endoscopy (WCE) and its extensive applications in clinic, doctors need to spend more time on reviewing the WCE images for lesions diagnosis. Therefore, automatic lesion detection for WCE has gradually become a research hotspot, which aims to reduce the pressure of doctors and improves the diagnosis efficiency. Many researchers adopted the traditional machine learning method to realize polyp detections, however, these methods need to extract the features manually, which were unable to find higher features of WCE images. So, in this study, we proposed a novel method that based on convolution neural network (CNN) to automatically recognize polyp in small bowel WCE image. We utilized the Alexnet architecture, one of the classical CNN, to extract the features of WCE images and classify polyp images from normal ones. We selected 14408 images from different patients, including 408 polyp images and 14000 normal images. Since the amount of initial polyp images is small, then, we did the data augmentation, including rotation, luminance change, blurring, and noise. At last the experimental results demonstrated that the method we proposed had a promising performance in polyp detection, whose accuracy, sensitivity and specificity can reach at 99.88%, 99.40% and 99.93%, respectively. Additionally, we evaluated ROC curve and its AUC value, which further confirmed that our model has a high accuracy and reliability in polyp detection. This proposed method has great potential to be used in the clinical examination to help doctors from the tedious image reviewing work.