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基于XGBoost的电影票房影响因素分析及预测研究
Analysis and Prediction of Influencing Factors of Movie Box Office Based on XGBoost

DOI: 10.12677/aam.2024.134164, PP. 1738-1745

Keywords: 电影票房预测,粒子群算法,XGBoost算法,PSO-BP神经网络模型
Box Office Forecast
, Particle Swarm Optimization, XGBoost Algorithm, PSO-BP Neural Network Model

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Abstract:

在我国电影产业中,电影票房是整个产业收益的主要来源,对票房进行准确预测对优化电影投融资,助力电影经营主体决策优化,促进整个电影产业的健康发展起着重要作用。本文主要构建了一种基于XGBoost算法筛选指标以及加入粒子群优化算法的BP神经网络的票房预测模型。首先,构建一个更全面的电影票房影响因素体系,加入微博因素和电影首日影评作为票房影响因素指标,同时结合电影特征和市场因素构建电影票房影响因素体系;其次,对各指标因素预处理量化后,为简化后期运算和提高模型的精度,构建基于XGBoost的影响力测量模型,并以此为依据进行筛选;最后,将筛选后的指标体系划分两部分即训练集和测试集,并在此基础上分别构建了BP神经网终模型、RBF模型以及PSO-BP模型,并引入评价指标和对案例电影预测精度进行分析,结果表明本文构建的PSO-BP模型具有更高的预测精度。本研究所构建的模型在电影上映期间预测最终票房具有一定的参考意义,可为有关部门提供决策参考。
In China’s film industry, the movie box office is the main source of revenue for the entire industry, and the prediction of the movie box office can optimize movie investment and financing, help the movie business entities to optimize their decision-making, and promote the whole film industry. This paper mainly constructs a box office prediction model based on XGBoost screening index and BP neural network with particle swarm optimization algorithm. Firstly, a more comprehensive box office influencing factor system is constructed, in which Weibo factor and the first day film review are added as box office influencing factor indicators, and at the same time, a box office influencing factor system is constructed by combining film characteristics and market factors; Secondly, after preprocessing and quantifying each index factor, in order to simplify the later operation and improve the accuracy of the model, the influence measurement model of influencing factors based on XGBoost algorithm is constructed, and the box office influencing factors are screened on this basis; Finally, in the empirical part of the movie box office prediction model, the data includes two parts: the training set and the test set, and the BP neural network final model, RBF model and PSO-BP model are constructed. The evaluation index is introduced and the prediction accuracy of case movies is analyzed. The results show that the PSO-BP model constructed in this paper has higher prediction accuracy. The model constructed in this study has certain reference significance in predicting the final box office during the film release period, and can provide decision-making reference for relevant departments.

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