Optimization of heterogeneous information real-time retrieval model of campus website

Most of the current information retrieval models of campus websites are centralized. In the case of large amount of information or more requests for access at the same time, it reduces the real-time performance of the retrieval and causes the bottleneck of the network. To address this problem, a new heterogeneous information real-time retrieval model of campus website is designed in this paper. The model of heterogeneous information network is introduced. LDAP directory technology is used to organize and manage global index information. A distributed heterogeneous information retrieval model of campus web site based on LDAP is designed and its physical architecture is built. The retrieval node is designed by using distributed structure of network token-ring and LDAP protocol. The queue is retrieved by using PopRank method and Co-Hits method. A large number of related heterogeneous information will be returned in the case of large difference between the retrieval key words and the required retrieval results, which need to be optimized. Rough classification of the retrieved results is achieved by clustering method to remove the result that is far apart from most of the retrieval results. The SOM neural network is used for fine classification to close the desired results and improve the real-time performance of retrieval. Experimental results show that the designed model is highly accurate and real-time. © 2020 Editura Politechnica. All rights reserved.