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The film industry is one of the core industries of the digital creative industry, which has great positive externalities to the digital creative economy. Movie box office revenue is an important indicator to measure the realization of the market value of movie consumption, and it is also the basic guarantee for the sustainable development of the movie industry. This paper relies on the professional database of the Maoyan movie market to use Python software to collect a total of 830 domestic movie-related consumption characteristic data from 2017 to 2019. In this study, the stacking method in the machine learning ensemble algorithm combines the fivefold crossfolding training method based on distributed random forest, extremely randomized trees, and generalized linear models. The model is good at handling different data types. It has higher fitting and model accuracy in feature mining and model construction, so as to effectively grasp the relevant feature factors affecting movie consumption and accurately predict the future movie box office. Based on the innovative design method of model fusion, the extracted feature vector is used to build a more accurate movie box office prediction model through stacking with a fivefold crossfolding training method. It is aimed at opening the black box that affects the realization of the value of the film content consumption market in the digital age and putting forward corresponding countermeasures and suggestions.

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Research on the Influencing Factors of Film Consumption and Box Office Forecast in the Digital Era: Based on the Perspective of Machine Learning and Model Integration