Facing the scenarios of distributed storage and cross-domain circulation of data in various business domains in industrial scenarios， a cloud-edge collaborative data storage and retrieval architecture was proposed to address the problems of multiple and complex business systems， huge amounts of data， and the inability of some data to be uploaded to the cloud， aiming to achieve unified storage and efficient cross-domain circulation of large-scale data. In this architecture， a data encoding rule based on the RDF （Resource Description Framework） graph model and a multi-level efficient data storage strategy based on S-tree （Signature-tree） were designed to ensure that data that cannot be uploaded to the cloud are stored in the edge server， and data that can be uploaded to the cloud are stored in the cloud server. Besides， a cloud-edge collaborative storage-oriented index tree efficient collaborative retrieval method based on cloud-edge collaborative retrieval CECI-tree （Cloud-Edge Collaboration Index-tree） was proposed to improve efficiency of data retrieval effectively through the cloud-edge collaborative indexing mechanism. Experimental results of comparing the proposed architecture with methods such as RDF-3X and GRIN show that the proposed architecture performs better in terms of running efficiency and CPU utilization.