Design of Intelligent Logistics Management System Based on Machine Learning

Abstract

In today's business environment, intelligent logistics management systems have become a key pillar in multiple industries, especially in industries such as construction and machinery. Computer-aided design (CAD) technology has proven its enormous potential in design support. In the vast field of logistics, CAD technology has shown remarkable potential in terms of precision and efficiency. In order to further enhance the intelligence level of logistics management, this article proposes a new design concept-combining CAD and machine learning technology to build an intelligent logistics management system. By using CAD technology, we can accurately plan logistics paths and optimize warehouse layout, thereby significantly improving the efficiency of goods transportation. This not only saves costs for enterprises but also improves overall operational efficiency, injecting new vitality into the development of the logistics industry. The core of this system lies in using CAD technology to construct accurate models of logistics scenes and analyzing and predicting massive logistics data through machine learning algorithms to achieve automation and intelligence in logistics management. Experimental data shows that compared with traditional support vector machine (SVM) algorithms, FSVM reduces the error rate by 32.17% and improves accuracy by 13.47% when processing different transaction sets. This result fully demonstrates the superiority of the FSVM algorithm in logistics data processing.