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In order to implement the application of machine learning in the field of cardiopulmonary resuscitation (CPR) into real clinical work, an assisted diagnosis and treatment algorithm based on machine learning and fused with CPR diagnosis and treatment standards was proposed. Random forest, gradient boosting tree and extreme gradient boosting were used as the base model. The voting method was used for model fusion, and the shapley additive explanation algorithm (SHAP) was introduced to filter out the features with lower shapley values for retraining, and the resulting model creates a parameter space under the cardiopulmonary resuscitation diagnostic and treatment standard for optimization, and finally obtains the optimal diagnostic and treatment plan. The resulting model was optimized by creating a parameter space under the CPR diagnosis and treatment criteria, and the optimal treatment plan was finally obtained. The results show that the algorithm after integrating the CPR diagnosis and treatment standards is more in line with the clinical reality, which can provide assistance for clinical diagnosis and treatment and improve the success rate of CPR. [ABSTRACT FROM AUTHOR]

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