A Deep Retrieval Model for Revit Software Database Based on Keyword Mining Algorithm

This research study explores the implementation of a deep retrieval model for the Revit software database, harnessing the power of keyword mining algorithms to enhance the search functionality and efficiency. By extracting and categorizing relevant keywords from voluminous textual data, this research study can effectively organize and condense information, paving the way for seamless text mining and information retrieval efforts. This methodology results in a robust and reliable database that greatly simplifies the process of searching and retrieving relevant information. For the model under consideration, this study defines a dual-core network to capture the weighted contributions of two core nodes to each other within a local network. Specifically, the dual-core network consists of nodes 1 and 2, which share a common relationship, along with their neighboring nodes that connect them. The edge weights between these nodes are carefully assigned to accurately reflect the relevance of the connected nodes to the keyword query throughout the retrieval process. By updating these edge weights, this study can achieve an effective retrieval model. To rigorously assess the performance of our proposed algorithm, this study adopted a collaborative programming approach that combines the strengths of Python and MATLAB. The results indicate that our algorithm exhibits remarkable proficiency, with an average time consumption of 6.3 seconds and an impressive accuracy level of 0.990.