The mature development and the extension of the industry chain make the income structure of the film industry. The income of the traditional film industry depends on the box office and also includes movie merchandising, advertisement, home entertainment, book sales etc. Movie merchandising can even become more profitable than the box office. Therefore, market analysis and forecasting methods for multi-feature merchandising of multi-type films are particularly important. Traditional market research is time-consuming and labour-intensive, and its practical value is restricted. Due to the limited research method, more effective predictive analysis technology needs to be formed. With the rapid development of machine learning and big data, a large number of machine learning algorithms for predictive regression and classification recognition have been proposed and widely used in product design and industry analysis. This paper proposes a high-precision movie merchandising prediction model based on machine learning technology: WE model. This model integrates three machine learning algorithms to accurately predict the movie merchandising market. The WE model learns the relationship between the movie merchandising market and movie features by analyzing the main feature information of movies. After testing, the accuracy rate of prediction and evaluation in the merchandising market reaches 72.5%, and it has achieved a strong market control effect.
翻译:工业链的成熟发展和扩展使电影业的收入结构成为电影业的收入结构。传统电影业的收入取决于盒式办公室,还包括电影销售、广告、家庭娱乐、图书销售等。电影销售甚至比盒式办公室更有利可图。因此,多类型电影的多功能机械化的市场分析和预测方法特别重要。传统市场研究耗时费时,劳力密集,其实际价值有限。由于研究方法有限,需要形成更有效的预测分析技术。随着机器学习和大数据的迅速发展,大量预测回归和分类识别的机器学习算法已经提出,并在产品设计和行业分析中广泛使用。本文提出了一个基于机器学习技术的高精度的电影销售放大预测模型:WE模型。这一模型综合了三种机器学习算法,以准确预测电影市场点火化市场。WE模型通过分析主要特征信息来了解电影市场和电影市场特征之间的关系。测试后,72.5的预测和评价的准确率达到了72.5的市场控制率。在72.5中实现了强有力的市场预测和评价。