As a new medium, Virtual Reality (VR) has attracted widespread attentions and research interests. More and more researchers have built their VR image/video database and devise related algorithms. However, the existing methods of VR video quality assessment are not very effective, and one of the most important reasons is that the database is not suitable. To this end, this paper proposes an efficient VR quality assessment method on self-built database. Firstly, we establish a VR video quality assessment database with subjective scores, and add the projection format to the production of the database. Secondly, the database is proved to be valid by using some traditional image quality assessment metrics. Lastly, we design a 3D convolutional neural network to predict the VR video quality without reference VR video. Meanwhile, taking the pre-processed VR video patches as input, different quality score strategy is applied to get the final score. The experimental results surface that the network we designed has good results, and the performance is improved after the weight calculation combined with the projection format.