  In recent years, driven by the Asian film industry, such as China and India,
the global box office has maintained a steady growth trend. Previous studies
have rarely used long-term, full-sample film data in analysis, lack of research
on actors' social networks. Existing film box office prediction algorithms only
use film meta-data, lack of using social network characteristics and the model
is less interpretable. I propose a FC-GRU-CNN binary classification model in of
box office prediction task, combining five characteristics, including the film
meta-data, Sina Weibo text sentiment, actors' social network measurement, all
pairs shortest path and actors' art contribution. Exploiting long-term memory
ability of GRU layer in long sequences and the mapping ability of CNN layer in
retrieving all pairs shortest path matrix features, proposed model is 14%
higher in accuracy than the current best C-LSTM model.
