Predicting pedestrian volume in campus public area is of significance for maintaining campus safety and improving campus management level. In particular, due to the outbreak of epidemic, the resumption of college education has put forward higher requirements for the prediction and control of the pedestrian volume in public area. Taking college canteens as an example, predicting the pedestrian volume in canteen is helpful with canteen epidemic prevention worker to make scheduling and arrangement, which not only reduces the risk of crowd gathering, but also provides more considerate service according to the distribution of the pedestrian volume in canteen. Considering the requirements of campus management, e.g., holiday, course arrangement, pedestrian volume prediction in the campus public area is challenging. This study proposes a multi-scale temporal patterns convolution neural networks (MSCNN) based on deep learning to obtain the short-term dependencies as well as long-term periodicities, and reweights the multi-scale temporal pattern characteristics to predict the pedestrian volume at any given time. The effectiveness and efficiency of the MSCNN model are verified by experiments on real-world datasets.