This is an extension from a selected paper from JSAI2019. Learning social influence between users on social networks has beextensively studied in a decade. Many models were proposed to model the microscopic diffusion process or to directly predict the final diffusion results. However, most of them need expensive Monte Carlo simulations to estimate diffusion results and some of them just predict the size of the spread via regression techniques, where people who will adopt the information becomes unknown. In this work, we regard the prediction of final influence diffusion results in a social network as a classification problem to avoid expensive simulations with knowing the final adopters. Furthermore, we propose a community-based convolutional neural network to capture the information of local structure with the aforementioned network. The proposed model is referred to as the Social Influence Learning on Community-based Convolutional Neural Network, SIL-CCNN. In the experiment, SIL-CCNN shows the promising results in both synthetic and real-world datasets. In addition, modeling local structure is indeed useful for the prediction of information diffusion.