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The mature development and the extension of the industry chain make theincome structure of the film industry. The income of the traditional filmindustry depends on the box office and also includes movie merchandising,advertisement, home entertainment, book sales etc. Movie merchandising can evenbecome more profitable than the box office. Therefore, market analysis andforecasting methods for multi-feature merchandising of multi-type films areparticularly important. Traditional market research is time-consuming andlabour-intensive, and its practical value is restricted. Due to the limitedresearch method, more effective predictive analysis technology needs to beformed. With the rapid development of machine learning and big data, a largenumber of machine learning algorithms for predictive regression andclassification recognition have been proposed and widely used in product designand industry analysis. This paper proposes a high-precision movie merchandisingprediction model based on machine learning technology: WE model. This modelintegrates three machine learning algorithms to accurately predict the moviemerchandising market. The WE model learns the relationship between the moviemerchandising market and movie features by analyzing the main featureinformation of movies. After testing, the accuracy rate of prediction andevaluation in the merchandising market reaches 72.5%, and it has achieved astrong market control effect.

WE model: A Machine Learning Model Based on Data-Driven Movie Derivatives Market Prediction