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For the limitation of the traditional load extrapolation methods in the process of load spectrum compilation, an adaptive bandwidth kernel density estimation algorithm was proposed based on the quad-tree algorithm to obtain the load spectrum, which can obtain the corn harvester frame more accurately and quickly. Firstly, the rain flow counting method was applied to count the pretreated measured load data. The load cycles whose amplitudes were less than 10% of the maximum load cycle amplitude value were filtered. The remaining load data were segmented into different regions according to the quad-tree segmentation algorithm. The Gaussian kernel function was selected as the kernel function, and the local optimal bandwidth of the data in each region was calculated according to the rule of thumb. In addition, the input of the kernel density estimation was optimized according to the density of data points in the data area, which reduced the calculation consumption of kernel density estimation. The measured load data from the frame of the corn harvester were used for verifying the effectiveness of proposed method. Compared with the traditional load extrapolation methods of fixed and adaptive bandwidth kernel density estimation, the proposed method greatly improved the computational efficiency, the probability density calculated by the proposed method was closer to the actual load distribution. The correlation coefficient of frequency distribution of load cycle mean and amplitude was closer to 1, and the root mean square error was smaller. The determining coefficient of the amplitude cumulative frequency curve was greater than 0.99. The results showed that the research result can provide reference for load extrapolation and load spectrum compilation.