Intelligent training systems have shown great potential in improving the efficiency and quality of battlefield rescue. This article aims to design and implement an intelligent training system based on these advanced technologies, specifically designed for the specific needs of battlefield rescue. The system design follows the principles of modularity, user interaction, and scalability, ensuring the efficiency, usability, and future development potential of the system. This article adopts computer vision technology for high-precision simulation of battlefield environment, and combines support vector machine (SVM) algorithm to provide personalized training scenarios and decision support. The performance evaluation of the system shows that the accuracy of computer vision technology and the efficiency of SVM algorithm have achieved good results, especially SVM algorithm has performed well in training and prediction. The experimental results show that the proposed intelligent training system can effectively improve the trainees’ battlefield adaptability and decision-making ability. The task completion rate of trainees increased from the highest 75% to 93.9%. At the same time, the students’ user satisfaction is generally high, and the average satisfaction of the training system exceeds 7.5 points. It can be seen that the intelligent training system designed in this article has a good effect in improving the efficiency of battlefield rescue training. The construction and application of the system not only improves the skills and reaction ability of rescuers, but also helps to improve their decision-making ability under different complex conditions on the battlefield.