This paper presents a new method for no reference mesh visual quality assessment using a convolutional neural network. To do this, we first render 2D images from multiple views of the 3D mesh. Then, each image is split into small patches which are learned to a convolutional neural network. The network consists of two convolutional layers with two max-pooling layers. Then, a multilayer perceptron (MLP) with two fully connected layers is integrated to summarize the learned representation into an output node. With this network structure, feature learning and regression are used to predict the quality score of a given distorted mesh without the availability of the reference mesh. Experiments have been successfully conducted on LIRIS/EPFL generalpurpose database. The obtained results show that the proposed method provides good correlation and competitive scores comparing to some influential and effective full and reduced reference methods.