Automated data processing, detection decisions, and inference decisions require efficient integration of multiple sensors and different information sources. However, due to environmental disturbances, sensor limitations, and human intervention, source information generally has strong uncertainty, incompleteness, and conflict, and is concentrated in evidence conflicts. Therefore, it is necessary to study the reasoning and decision-making of conflict evidence. In order to solve the problem that evidence theory cannot integrate conflict evidence effectively, a new uncertainty measurement method is proposed in this paper. First of all, through an in-depth analysis of the existing uncertainty measurement formulas based on information entropy and interval distance, the defects of the existing algorithms are summarized and proved. Then based on definite integral, a new interval distance measurement formula is defined and the rationality of this formula is proved. Furthermore, based on the proposed interval distance formula, a new uncertainty measurement method is presented and the conflict evidence combination rule and algorithm flow are given using the improved uncertainty measurement method. Finally, the feasibility and effectiveness of the improved method are illustrated by an example.