In modern trauma treatment， reasonable and accurate pre-hospital assessment based on the injury and making corresponding treatment decisions are of great significance for reducing the disability and mortality of patients. To improve the shortcomings of manual decision-making and achieve accurate and reasonable standardized trauma treatment decision-making， after in-depth analysis and research on the treatment decision， this study uses the multi-label learning method to divide the overall treatment decision into sub-decisions， and extracts judgment factors corresponding to the sub-decisions as a label sets. Next， to better consider the relationship between labels， this paper combines the chain idea of the Classifier Chains algorithm with the ML-KNN algorithm， and proposes a multi-label learning algorithm by improving the ML-KNN algorithm， named layer chains multi-label K-nearest neighbor （LCML-KNN）. The LCML-KNN algorithm divides labels into two layer chains according to the characteristics. After the prediction label information of the first layer chain is output， it is uniquely encoded. And the transformed lables are put into the second layer chain as new features for prediction and judgement. The LCML-KNN algorithm not only better takes into account the relationship between the labels but also expands the feature dimension through the label conversion. The experimental results with various existing multi-label learning algorithms on two trauma datasets verify the robustness and superiority of the LCML-KNN algorithm.