Through online social networks, members can create events and invite others to join. However, predicting the range of members who will attend upcoming events is a challenging problem. This paper studies the problem of event scale prediction in event-based social networks (EBSNs), which is crucial for event organizers to host events. Firstly, we investigate the characteristics of EBSN to extract three main factors of events. Then, we generate features based on three factors and use the K-means method to cluster participants of events into three different event scales. Next, we obtain a dataset; each sample of this dataset represents one event by a vector of 18 features and one scale label. We take the obtained dataset to train predictive classifiers. Finally, when a new event is coming, we use trained models to predict the scale of this event. Extensive experiments were conducted on a real crawled Meetup event dataset, and results have illustrated the effectiveness of our method in terms of accuracy. Moreover, we find that the content factor is the most critical factor that affects the performance of models and the decisions of members.