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Aiming to enhance the applicability and accuracy of the cutting depth control system for sugarcane harvesters, a load pressure prediction model was established to address the problem that the current reference pressure setting could not be automatically adjusted according to soil parameters and locomotive parameters. The relationship between the load pressure and the cutting depth into the soil, the feeding volume, the soil moisture content and the soil firmness was collected by orthogonal test methods, and the test data were used as the training samples and test samples of the load pressure prediction model. Based on the training samples, load pressure prediction models using extreme learning machine (ELM) and ELM based on sparrow search algorithm optimization (SSA-ELM)were established. Performance of the prediction model was evaluated by the test samples, and the results showed that compared with the ELM model, the mean absolute error, mean relative error and root-mean-square error of the SSA-ELM prediction model were reduced by 50.00%, 44.14% and 44.44% under the yellow soil condition, and reduced by 58.33%, 56.98% and 57.14% under red soil conditions. To verify the applicability of the load pressure prediction model in actual harvesting processes,various working conditions encountered in the cane field were simulated on the test platform, and the prediction model was applied to the existing control system for testing. The results showed that the prediction model met the setting requirements of the reference pressure when the cutting depth into the soil was 20mm, the operating speed was 0.34m/s, and the rotational speed of the cutter disc was 700r/min, and the maximum error between the cutting depth and the target depth was no more than 5mm, which met the actual requirements of sugarcane harvesting production.