Objective: The paper established a cost weight model for the group of "cholecystectomy without complications and comorbidities" within a hospital based on machine learning technology, and analyzed the reasonable cost composition in this group to find methods for controlling and reducing hospitalization costs in practice. Methods: Based on the DRG management plan released by Chongqing City in 2021, medical record data of HC35 groups from January 2021 to April 2024 were collected. According to the ratio of total hospitalization expenses to group compensation standards, they were divided into all data group, 30% ~100% normal compensation group, and 90% ~100% standard compensation group. Then, based on the classification of price modules, various cost sets were established. Gradient descent technique was used to analyze the weight of each price module in the cost and establish an analysis model. Statistical hypothesis testing was used to verify the operation data, and the final weight model adopted was obtained after analysis. After confirming the model, the previous data was regrouped and calculated using the DRG management plan released by Chongqing City in 2024 to obtain new grouping results. Additionally, medical record data for HC25 grouping after April 2024 was collected, and the latest cost weight and proportion were obtained through model fitting. Results: The data model used is a 90% ~100% standard compensation group model, with the top module weight being (consumables cost 1.343 [28.09%], treatment cost 1.170 [24.47%], western medicine cost 0.846 [17.70%], testing and examination cost 0.765 [16.00%], medical service cost 0.459 [9.69%]). Afterwards, discussions will be conducted accordingly on various connotations to explore reasonable cost control methods. Conclusions: Combining information technology with clinical pathways and other control methods to establish DRG grouping cost management model is a beneficial operational behavior that combines theory with practice. It can be widely promoted in medical institution cost management in the future.