The study primarily focuses on the optimization of query scheduling within economic dynamic systems, which are characterized by vast, sensitive datasets that necessitate exceptional accuracy and reliability. As the digital age accelerates data proliferation, the complexity of data retrieval escalates, leading to critical challenges such as sluggish query speeds and inconsistent results in existing systems. Traditional query algorithms, which rely on basic search logic such as keyword matching and hyperlink analysis, have been proven unsuitable for enterprise-scale operations, often resulting in delays and inaccurate data. To address these deficiencies, an Improved Query Scheduling (IQS) algorithm is embedded within an economic dynamic system model. The IQS leverages continuous data reading capabilities to enable seamless algorithm execution, coupled with weighted screening of query node loads. This approach significantly reduces central processing unit strain while enhancing query efficiency. The experimental results demonstrated the effectiveness of the model, which processed multiple combinations of query features with a CPU execution time of less than 5 seconds and achieved nearperfect query accuracy (0.99). In real-world deployments, the model maintained stable load distribution, completing platform-level queries in 16.56 seconds at a spatial frequency of 1. These findings underscore the model’s superior query scheduling capabilities for economic dynamic systems, confirming its ability to efficiently handle routine economic data queries with precision and reliability. This advancement offers a robust solution to the persistent challenges of dataintensive economic environments.