Music Emotion Classification Model Based on Multi Feature Image Fusion

With the development of modern technology, the field of music emotion recognition has also made continuous progress, ushering in a series of innovative methods. This article proposes a music emotion classification model based on multi feature image fusion, aiming to improve the practical application ability of the model by integrating multiple music features and image information. By constructing a multimodal dataset containing audio and video features and designing a fusion strategy, our model can more accurately identify the emotional attributes of music. The experimental results show that the multi feature fusion model proposed in this paper performs well in music emotion classification tasks. The overall classification accuracy ranges from $95.02 \%$ to $99.89 \%$, demonstrating the high stability and accuracy of the model. The comparative analysis of click through rate and repeat playback rate further confirms the advantages of multi feature fusion strategy in improving user experience.