Machine Learning-Based Knowledge Trend Analysis Using LDA for Strategic Decision-Making

Abstract

In the digital economy, Knowledge Management Systems (KMS) often fail to provide actionable insights due to information overload, leaving valuable expertise fragmented and underutilized. This research aims to integrate Machine Learning (ML) to transform passive data into proactive strategic foresight by analyzing knowledge trends. Using a longitudinal dataset of search logs and document metadata, the study implements a text-mining pipeline centered on Latent Dirichlet Allocation (LDA) to extract thematic clusters. The model identified eight distinct knowledge domains, with "Advanced Data Analytics" emerging as a high-growth sector (TVI = +0.13), while a critical "Knowledge Gap" in cybersecurity was detected where search demand outpaced document supply by 58%. This study contributes by proposing a Trend Velocity Index (TVI) to quantify knowledge evolution and detect knowledge gaps, providing a robust framework for leaders to optimize resource allocation and ensure institutional agility.