Musical structure design relies heavily on fixed musical forms and creative models, leading to homogeneity in both form and content and low musical quality. Therefore, this study investigates music structure design based on JavaScript. Music generation utilizes advanced algorithmic models to provide input. Generative adversarial networks are a commonly used deep learning model consisting of a generator and a discriminator, which are iteratively optimized to produce high-quality musical sequences. JavaScript can integrate real-time audio processing technology to build audio processing pipelines and design music structures. Experiments have shown that in music structure design based on it, the main area of pitch distribution accounts for 75%, far exceeding the comparison method; The distribution of average pitch changes is balanced, and the density of banknotes is stable and high. The music quality evaluation indicators, low pitch rate of 5%, level 8, qualified note rate of 92%, and rhythm complexity of 0.75, indicate that it produces significant high-quality music effects.
