The composition of the dataset.

This study aims to develop a digital retrieval system for art museums to solve the problems of inaccurate information and low retrieval efficiency in the digital management of cultural heritage. By introducing an improved Genetic Algorithm (GA), digital management and access efficiency are enhanced, to bring substantial optimization and innovation to the digital management of cultural heritage. Based on the collection of art museums, this study first integrates the collection’s images, texts, and metadata with multi-source intelligent information to achieve a more accurate and comprehensive description of digital content. Second, a GA is introduced, and a GA 2 Convolutional Neural Network (GA2CNN) optimization model combining domain knowledge is proposed. Moreover, the convergence speed of traditional GA is improved to adapt to the characteristics of cultural heritage data. Lastly, the Convolutional Neural Network (CNN), GA, and GA2CNN are compared to verify the proposed system’s superiority. The results show that in all models, the sample output results’ actual value is 2.62, which represents the real data observation results. For sample number 5, compared with the actual value of 2.62, the predicted values of the GA2CNN and GA models are 2.6177 and 2.6313, and their errors are 0.0023 and 0.0113. The CNN model’s predicted value is 2.6237, with an error of 0.0037. It can be found that the network fitting accuracy after optimization of the GA2CNN model is high, and the predicted value is very close to the actual value. The digital retrieval system integrated with the GA2CNN model has a good performance in enhancing retrieval efficiency and accuracy. This study provides technical support for the digital organization and display of cultural heritage and offers valuable references for innovative exploration of museum information management in the digital era.本研究旨在开发一款面向艺术博物馆的数字检索系统，以解决文化遗产数字化管理中存在的信息不准确、检索效率低下等问题。通过引入改进型遗传算法（Genetic Algorithm, GA），提升数字化管理与访问效率，为文化遗产数字化管理带来实质性优化与创新。本研究以艺术博物馆馆藏为基础，首先将馆藏的图像、文本与元数据与多源智能信息进行融合，以实现对数字内容更为精准且全面的描述。其次，引入遗传算法，并提出一种结合领域知识的GA2卷积神经网络（GA2 Convolutional Neural Network, GA2CNN）优化模型。此外，为适配文化遗产数据的特性，对传统遗传算法的收敛速度进行了优化。最后，通过对比卷积神经网络（Convolutional Neural Network, CNN）、遗传算法以及GA2CNN模型，验证所提出系统的优越性。研究结果显示，在所有模型中，样本输出结果的真实值为2.62，该值代表真实数据观测结果。对于第5号样本，相较于2.62的真实值，GA2CNN与GA模型的预测值分别为2.6177与2.6313，对应的误差分别为0.0023与0.0113；卷积神经网络模型的预测值为2.6237，误差为0.0037。可以发现，经GA2CNN模型优化后的网络拟合精度较高，预测值与真实值极为接近。集成GA2CNN模型的数字检索系统在提升检索效率与准确性方面表现优异。本研究为文化遗产的数字化组织与展示提供了技术支撑，也为数字时代博物馆信息管理的创新探索提供了极具价值的参考。