Agricultural Information Management Data Model Construction Based on Cloud Computing and Semantic Technology

Abstract

In order to improve the data retrieval and mining ability of agricultural information management system, an agricultural information management data model based on cloud computing and semantic technology was proposed. Fuzzy C-means algorithm is used for adaptive fusion and clustering of semantic association features of distributed data in large-scale information management systems; feature compressors are used to reduce the storage space dimension of large-scale information management system to improve the target data mining capability and adaptive scheduling capability of information management system. The registration rate improves than the traditional method by 12.46% and computational cost by 23.76%. This method has higher accuracy in storage data mining and retrieval for large information management systems and superior performance than the traditional method. The introduction of the concept and technical methods of related data is the best practice to realize the fine disclosure, standardized description, semantic organization, and in-depth integration of massive agricultural science and technology information resources, which will play an important role in improving the visibility, visibility, and accessibility of agricultural information resources. It is innovative in the construction of the multidimensional semantic-related model and field knowledge service system driven by related data.