Abstract: In order to achieve the intelligent height adjustment control of the shearer, the key techniques are the coal-rock interface recognition, cutting path optimization, and shearer height adjustment control. Although the coal-rock interface is accurately identified, the shearer drum cannot completely follow the estimated coal-rock interface due to the flatness requirement for the roof and floor of the coal seam which guarantees the working of hydraulic supports. Therefore, the cutting trajectory should be optimized based on the coal-rock interface recognition results, which is regarded as the target trajectory of shearer height-adjusting control. To solve this issue, the cutting path optimization is required. Based on the estimated coal-rock interface and considering the limitations in practical application, the cutting path is optimized to maximize the recovery ratio. To improve the optimization results, satisfy the restricting condition, and reduce the computational complexities, this paper proposed a novel constraint optimization method of shearer cutting path based on the mayfly algorithm and B-spline curve fitting. A novel objective function is built, in which the curve node coefficients are chosen as the design variants, and the optimization target is minimizing the difference between the fitness curve and the coal-rock interface, leading to the much less designed variants and the lower computational load. The piecewise penalty function is used to deal with the constraints, which assists the exploration process in escaping from local maxima and make sure the constraints work. And then the modified mayfly algorithm is used to find the optimized cutting path to further improve the optimization effect and the convergence rate. Finally, the simulations of cutting path optimization are conducted under the condition of folds, subsidence and faults, which indicate that the proposed method can obtain the optimized smooth cutting path with the limitation of the constraints quickly, and have high real-time behavior, and good applicability.