Cervical tumor segmentation on 3D <sup>18</sup>FDG PET images is a challenging task due to the proximity between cervix and bladder. Since bladder has high capacity of <sup>18</sup>FDG tracers, bladder intensity is similar to cervical tumor intensity in the PET image. This inhibits traditional segmentation methods based on intensity variation of the image to achieve high accuracy. We propose a supervised machine learning method that integrates a convolutional neural network (CNN) with prior information of cervical tumor. In the proposed prior information constraint CNN (PIC-CNN) algorithm, we first construct a CNN to weaken the bladder intensity value in the image. Based on the roundness of cervical tumor and relative positioning information between bladder and cervix, we obtain the final segmentation result from the output of the network by an auto-thresholding method. We evaluate the performance of the proposed PIC-CNN method on PET images from 50 cervical cancer patients whose cervix and bladder are abutting. The PIC-CNN method achieves a mean DSC value of 0.84 while transfer learning method based on fully convolutional neural networks (FCN) achieves 0.77 DSC. In addition, traditional segmentation methods such as automatic threshold and region-growing method only achieve 0.59 and 0.52 DSC values, respectively. The proposed method provides a more accurate way for segmenting cervical tumor in 3D PET image.