To reduce the cost of music creation, shorten the creation time, help composers create music more easily, and make the style of music works more easily accepted by more people, based on the principle of symmetry, network analysis and ANN (artificial neural network) algorithm are applied to design children's music creation system based on GA (genetic algorithm) and ANN. The system includes human–computer interaction technology, learning mechanism, and distributed architecture. To accurately describe music information and extract different types of music features, the Back-Propagation method is used to establish the music classification model. The system helps musicians to create music in real time, overcomes the contradiction between people's evaluation and music generation speed, and retains the advantages of traditional GA. The results show that, compared with other algorithms, the average accuracy of the computer-aided composition technology based on GA and ANN is 97.5%, the best performance in recall rate and synthesis is 94.35% and 93.134%, respectively, and the algorithm model is effective. Moreover, the simulation results of this algorithm show that it is helpful to improve the composition technology and help composers more easily create music, making the style of music works more acceptable to people. This study can provide theoretical basis and practical significance for the research of computer-aided music creation.

为了减少音乐创作的成本，缩短创作时间，帮助作曲家更轻松地创作音乐，并基于对称性，网络分析和ANN（人工神经网络）原理，使音乐风格更容易为更多人所接受。该算法被应用于基于遗传算法和人工神经网络的儿童音乐创作系统的设计。该系统包括人机交互技术，学习机制和分布式体系结构。为了准确地描述音乐信息并提取不同类型的音乐特征，使用了反向传播方法来建立音乐分类模型。该系统帮助音乐家实时创作音乐，克服了人们的评价与音乐生成速度之间的矛盾，并保留了传统GA的优势。结果表明，与其他算法相比，基于遗传算法和人工神经网络的计算机辅助合成技术的平均精度为97.5％，召回率和综合性能最佳，分别为94.35％和93.134％，算法模型是有效的。此外，该算法的仿真结果表明，它有助于改进作曲技术，并有助于作曲家更轻松地创作音乐，使音乐作品的风格更受人们的接受。该研究可以为计算机辅助音乐创作的研究提供理论依据和实践意义。算法模型是有效的。此外，该算法的仿真结果表明，它有助于改进作曲技术，并有助于作曲家更轻松地创作音乐，使音乐作品的风格更受人们的接受。该研究可以为计算机辅助音乐创作的研究提供理论依据和实践意义。算法模型是有效的。此外，该算法的仿真结果表明，它有助于改进作曲技术，并有助于作曲家更轻松地创作音乐，使音乐作品的风格更受人们的接受。该研究可以为计算机辅助音乐创作的研究提供理论依据和实践意义。