The activeness of regional business entities, like restaurants, cinemas and shopping malls, represents the evolvement of their corresponding commercial districts, whose prediction helps practitioners grasp the trend of commercial development and provides support for urban layout. On the other hand, online social network services, such as Yelp, are generating massive online reviews toward business entities every day, which provide a solid data source for the prediction of regional commercial activeness and entity condition through big data technology rather than applying business data with limited access and poor time efficiency. Inspired by the outstanding performance of deep learning in the field of image and video processing, this paper proposes a deep spatio-temporal residual network (DSTRN) model for regional commercial activeness prediction using online reviews and check-in records of commercial entities. Furthermore, aiming at predicting business trend of entities, we also propose a novel multi-view entity condition prediction model (SBCE) based on online views, along with business attributes and regional commercial activeness. The experiments on the public Yelp datasets demonstrate that both DSTRN and SBCE outperform the compared approaches.