Abstract: In general, the identification of working conditions of agricultural machinery had significant research value in refining the working conditions of agricultural machinery and helping to master the trend of regional pollutant discharge. Based on the time series of the tractor running speed, engine speed, and real-time fuel consumption under different running conditions, the research introduced the image recognition method into tractor working condition identification for the first time. At the same time, the research also applied the parameter optimized support vector machine and the convolutional neural network（CNN） to conduct a systematical study related to the tractor working conditions. The related research results indicated that a support vector machine based on parameter optimization could realize the working condition identification of sample points in an ideal way, with the recognition accuracy reaching 99.851 9%. Nevertheless, it cannot realize the continuous identification of agricultural machinery working conditions, nor can it effectively identify the conversion stage of agricultural machinery working conditions. Moreover, in this study, a range of information, including tractor running speed and engine speed, are used to construct the sample image, thereby describing the data expression of agricultural machinery working condition change. The application of the convolutional neural network（CNN） is beneficial to realize the continuous recognition of agricultural machinery working conditions effectively, with the recognition accuracy reaching 93.3%. In short, the research not only provided reference value for the research related to the identification of agricultural machinery working conditions but also provided corresponding technical support for the subsequent research on the regional pollutant emissions produced by agricultural machinery under different working conditions.