Abstract
Because of the unique attributes of archive information, it is challenging to manage and effectively retrieve archive information in the archive information management practice. This paper designs and develops the first general higher-order Neural Network Model for archives. Based on the analysis of the correlation, the relevance of the weight model, the study of technical methods about the core weight, the direction weight retrieval, and the statistical ranking of the results, this paper designs a corresponding archive information analysis system. Finally, this paper adopts the B/S development model by applying the relevance ranking weight algorithm into the comprehensive archive retrieval activities, which not only enhances the intelligence and efficiency of the archive retrieval, but also can act as a standard example to demonstrate informatization construction for archive management. This paper compares this algorithm with two other existing retrieval algorithms and verifies the practicability of the relevance algorithm by evaluating the algorithm and the default retrieval algorithm using the NDCG evaluation method.
Highlights
Information retrieval is a process and a technique that allows information users to find relevant information they need through a dataset, where information is organized in a certain way [1]–[3]
This verifies the practicability of the relevance algorithm by evaluating the algorithm applied in this paper and the default retrieval algorithm through the Normalized Discounted Cumulative Gains (NDCG) evaluation method
The results representing investigation category by using the proposed method were slightly lower than those using NDCG [23], they were higher than the results presented by other gain indicators
Summary
Information retrieval is a process and a technique that allows information users to find relevant information they need through a dataset, where information is organized in a certain way [1]–[3]. This paper constructs a Neural Network Model for archive information retrieval, which can support users’ self-retrieval by simplifying the searching queries, and can efficiently filter redundant information that affects the readability of the retrieval results. This paper includes four parts: the first part explains the motivation to construct the New Incentive Neural Network Model; the second part illustrates the creation of theoretical propositions of the Neural Network Model construction; the third part describes and analyzes the archive case study by applying this proposed model; and the fourth part compares this algorithm with two other existing retrieval algorithms This verifies the practicability of the relevance algorithm by evaluating the algorithm applied in this paper and the default retrieval algorithm through the Normalized Discounted Cumulative Gains (NDCG) evaluation method. Using Volterra series theory, it can be proved that a higher-order neural network suitably high enough can realize arbitrary nonlinear transformations within the precision range
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