![]() The prediction model using the BP neural network has the following shortcomings. A BRP feedback neural network was proposed to solve movie box office prediction and classification problems. The competition factors with similar movie release time on the standard regression framework were tested, and a more simplified empirical model was proposed. They concluded that celebrity influence is positively related to box office. They analyzed the influence of celebrity effect on box office. A single influencing factor of the movie box office was mainly analyzed. A new method of movie box office prediction based on two-level and twice proxy variables was proposed, which can predict the first weekʼs box office by using some preindicators obtained before the movie is released. Linear regression and nonlinear regression models used to construct social media-driven movie box office prediction models were proposed. At present, the prediction of the movie box office has become one of the hottest research by scholars. The movie box office, as an indicator of the level of film development, has attracted great attention from all walks of life. ![]() The results show that the relative error of LSTM-AACS prediction is 6.58%, which is lower than other models used in the experiment. Finally, 10,398 pieces of movie box office dataset are used in the Kaggle competition to compare the prediction results with the LSTM-AACS model, LSTM-Attention model, and LSTM model. Second, we establish an LSTM (Long Short-Term Memory) box office prediction model and inject the attention mechanism to construct an adaptive attention with consumer sentinel for movie box office prediction. Tackling the problem of ignoring consumer groups in existing prediction models, we add consumer features and then quantitatively analyze and normalize the box office influence factors. First, the influencing factors of the movie box office are analyzed. To improve the movie box office prediction accuracy, this paper proposes an adaptive attention with consumer sentinel (LSTM-AACS) for movie box office prediction.
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