Analysis of Multi-Intelligent Distributed Japanese Language Block Recognition Based on Knowledge Recognition Corpus

Abstract

The Knowledge Recognition Corpus (KRC) serves as a comprehensive repository designed to capture and organize diverse forms of knowledge for computational analysis and understanding. This corpus encompasses a wide array of data sources, including text documents, images, videos, and structured datasets, covering various domains and topics. The concept of Multi-Intelligent Distributed Knowledge Recognition Corpus (MI-DKRC) represents an innovative approach to harnessing the collective intelligence of distributed systems for knowledge recognition tasks. This paper presents an analysis of Multi-Intelligent Distributed Japanese Language Block Recognition (MI-DJLBR) based on the Knowledge Recognition Corpus (KRC), employing multi-factor bi-gram Sentimental Classification (MFbi-SC). The MI-DJLBR system is designed to recognize and classify Japanese language blocks within a distributed framework, leveraging the collective intelligence of multiple intelligent agents. Through the utilization of the KRC, which encompasses a diverse array of Japanese text samples, images, and multimedia content, MI-DJLBR aims to enhance the efficiency and accuracy of Japanese language block recognition tasks. The incorporation of MFbi-SC further refines this process by considering multiple factors, including syntax, semantics, context, and sentiment, to classify Japanese language blocks with greater precision. Simulation demonstrated that a dataset of 1,000 Japanese language blocks, MI-DJLBR achieves an average recognition accuracy of 94.5%, demonstrating its effectiveness in accurately identifying and classifying Japanese text segments. Furthermore, the incorporation of MFbi-SC enhances the system's classification accuracy by an average of 7.2%, indicating the significance of multi-factor sentiment analysis in refining classification outcomes. In terms of computational efficiency, MI-DJLBR exhibits impressive processing times, with an average recognition speed of 200 blocks per second. This highlights the scalability and responsiveness of the distributed framework, enabling efficient processing of large-scale Japanese language datasets in real-time scenarios.