Exploring the Integration of National Cultural Resources through Content Retrieval and Visual Semantic Segmentation Display

In the era of ubiquitous information and abundant data, the ability to extract relevantinformation from scattered, obscure, and heterogeneous data sources is of paramount importance. Despitethe considerable richness of ethnic cultural resources, they are often characterized by significant heterogeneity and fragmentation. Semantic segmentation technology can effectively leverage the power of computers to extract image features and compile feature databases, enabling the fusion of heterogeneous knowledge. To this end, this paper proposes an image classification method that combines object semantics and deep learning algorithms to identify object areas within an image and retrieve matching images from a database based on implicit semantic information. Through the integration of national cultural resources, this approach enables content retrieval and visual semantic segmentation. The use of multi-layer perceptrons for supervised learning facilitates the generation of object semantic templates to address the challenge of object semantic similarity. The resulting templates can then be applied to classify images, leading to a high classification accuracy rate for binary semantic images. Experimental results demonstrate that the proposed method enables automatic feature comparison with target images in the feature library during the retrieval of ethnic cultural resources, allowing for the mining of previously undiscovered and valuable knowledge. Finally, the best matching results and relevant information are output to users, enabling the effective use of these resources