The TV show rating analysis and prediction system can collect and transmit information more quickly and quickly upload the information to the database. The convolutional neural network is a multilayer neural network structure that simulates the operating mechanism of biological vision systems. It is a neural network composed of multiple convolutional layers and downsampling layers sequentially connected. It can obtain useful feature descriptions from original data and is an effective method to extract features from data. At present, convolutional neural networks have become a research hotspot in speech recognition, image recognition and classification, natural language processing, and other fields and have been widely and successfully applied in these fields. Therefore, this paper introduces the convolutional neural network structure to predict the TV program rating data. First, it briefly introduces artificial neural networks and deep learning methods and focuses on the algorithm principles of convolutional neural networks and support vector machines. Then, we improve the convolutional neural network to fit the TV program rating data and finally apply the two prediction models to the TV program rating data prediction. We improve the convolutional neural network TV program rating prediction model and combine the advantages of the convolutional neural network to extract effective features and good classification and prediction capabilities to improve the prediction accuracy. Through simulation comparison, we verify the feasibility and effectiveness of the TV program rating prediction model given in this article.

中文翻译：

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基于卷积神经网络的电视节目收视率预测模型

电视节目收视率分析和预测系统可以更快地收集和传输信息，并将信息快速上传到数据库。卷积神经网络是模拟生物视觉系统运行机制的多层神经网络结构。它是由顺序连接的多个卷积层和下采样层组成的神经网络。它可以从原始数据中获取有用的特征描述，并且是从数据中提取特征的有效方法。目前，卷积神经网络已成为语音识别，图像识别与分类，自然语言处理等领域的研究热点，并已在这些领域得到广泛成功的应用。所以，本文介绍了卷积神经网络结构来预测电视节目收视率数据。首先，它简要介绍了人工神经网络和深度学习方法，并重点介绍了卷积神经网络和支持向量机的算法原理。然后，我们改进卷积神经网络以适应电视节目收视率数据，最后将这两种预测模型应用于电视节目收视率数据预测。我们改进了卷积神经网络电视节目收视率预测模型，并结合了卷积神经网络的优势，提取了有效特征，并具有良好的分类和预测能力，提高了预测精度。通过仿真比较，验证了本文给出的电视节目收视预测模型的可行性和有效性。