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With the problems of severe tool wear and lack of fault monitoring device, leading to the failure of the work during the working process of stalk chopping, an intelligent monitor system which can be mounted on the returning stalk chopping machine was designed. Taking STM32 microcontroller as the main controller, multiple sensors fusion technology was applied, and tool wear condition monitoring was realized based on machine learning algorithm. In order to solve the problem of difficult extraction of nonlinear feature signals of straw crushing tool wear, a method of tool wear monitoring IBOA-SVM integrating improve butterfly optimization algorithm (IBOA) and support vector machine (SVM) was proposed. The monitoring method used the rotational speed, left side vibration frequency, and right side vibration frequency of the crushing knife roll as input eigenvectors to the model, and the wear condition of the tool (normal, worn and lost) as outputs. Compared with the unoptimized SVM algorithm, the identification accuracy of tool wear condition was improved from 95.61% to 98.83% by optimizing the parameters of the SVM algorithm with the IBOA algorithm. In order to verify the effectiveness of the IBOA-SVM model, the repeated comparison experiments of multiple models were conducted under the same parameter setting environment, which showed that the average value of the recognition accuracy of the IBOA-SVM model was improved and the accuracy of a single trial was maintained at a high level as compared with the five models of SVM, PSO-SVM, WOA-SVM, BOA-SVM and CWBOA-SVM. The IBOA-SVM model was embedded into the monitoring system and field test was conducted, in which it was shown that the designed tool wear condition monitoring system had good performance both in terms of recognition accuracy and robustness.