Animation has a rich history of over a century, and scholarly interest in the critique of animated films has evolved alongside its development. With the advent of the Internet and advancements in computer technology, traditional methods of animated movie appreciation are increasingly challenged by new complexities. This paper addresses these challenges by presenting an advanced intelligent system for evaluating animated films, utilizing cutting-edge deep learning models. Our approach begins with a pre-trained deep learning model to classify image categories and extract scene characteristics, while a primary color extraction algorithm captures key color features. Building on this, we propose an innovative deep convolutional neural network (CNN) augmented with fully connected layers to integrate multimodal features, enhancing the accuracy of film evaluations. Furthermore, we introduce a novel deep learning framework designed to detect pixel regions in images that evoke emotional responses. By employing visualization techniques within the convolutional network, we identify emotionally significant regions and enhance these features during initial training, improving the system’s emotional sensitivity. Experimental results demonstrate that our system not only improves the accuracy and efficiency of animated film evaluations but also surpasses existing methods, offering a more nuanced, emotion-aware approach to animation critique. This work advances the field by providing a robust, automated tool for evaluating animated films, offering deeper insights into their emotional and visual components, and contributing to the ongoing evolution of AI-driven film appreciation systems.
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