Abstract: High-quality seedbed preparation is essential for soybean wide–narrow-row dense planting in the Shajiang black soil region of northern Anhui Province, China. Shajiang black soil is characterized by high cohesion, poor fragmentation, and pronounced changes in physical properties under different moisture conditions, which can result in insufficient soil crushing, unstable ridge formation, and excessive soil backflow during seedbed preparation. To improve the quality and stability of micro-ridge seedbeds under these conditions, a soybean micro-ridge seedbed preparation device for wide–narrow-row dense planting was designed. The device mainly consisted of a dual-shaft differential rotary tillage device, a three-segment gradient ridging device, and a pressing and shaping device. During operation, the low-speed rotary tiller rotor performed primary soil fragmentation, whereas the high-speed rotor further fragmented the disturbed soil and conveyed it toward the ridge-forming region. According to the transmission relationship, the rotational speed of the high-speed rotor was approximately 1.40 times that of the low-speed rotor. Based on the agronomic requirements of soybean wide–narrow-row dense planting, the target micro-ridge was designed with a ridge-top width of 300 mm, a ridge-bottom width of 520 mm, and a ridge height of 200 mm. Four pressing and shaping units were arranged transversely to correspond to the four ridge-forming regions and to complete ridge-top pressing and ridge-side shaping. Field tests were conducted in the Shajiang black soil region. The ridging plate inclination angle, rotational speed of the low-speed rotary tiller rotor, and rotary tillage depth were selected as experimental factors, while ridge qualification rate and soil backflow rate were used as evaluation indicators. Single-factor tests were first conducted to determine suitable factor ranges, followed by a three-factor, three-level response surface experiment. Quadratic regression models for ridge qualification rate and soil backflow rate were established using Design-Expert 13.0. Analysis of variance showed that both models were highly significant, whereas the lack-of-fit terms were not significant, indicating that the established models were suitable for prediction and parameter optimization. According to the F values of the linear terms, the effects of the three factors on both evaluation indicators followed the same order: rotational speed of the low-speed rotary tiller rotor, ridging plate inclination angle, and rotary tillage depth. Multi-objective optimization was then performed by maximizing ridge qualification rate and minimizing soil backflow rate. The theoretical optimum was obtained at a ridging plate inclination angle of 25.64°, a low-speed rotary tiller rotor speed of 296.5 r/min, and a rotary tillage depth of 149.7 mm, corresponding to predicted ridge qualification rate and soil backflow rate of 97.27% and 3.55%, respectively. Considering machine adjustment accuracy and field operability, the parameters used for field verification were adjusted to 26°, 292 r/min, and 150 mm, respectively. Three field verification tests were conducted under the adjusted parameter combination. The measured mean ridge qualification rate and soil backflow rate were 96.85% and 3.68%, respectively, and the relative errors compared with the theoretical predictions were 0.43% and 3.66%, both below 5%, confirming the reliability of the optimized parameter combination. UAV-borne LiDAR was further used to acquire point-cloud data and reconstruct the seedbed surface in three dimensions. Five sampling areas were arranged within the stable operating region, and two ridge cross-sections were randomly extracted from each area, resulting in a total of ten measured cross-sections. After horizontal alignment and elevation normalization, the measured cross-sectional profiles were superimposed on the standard ridge profile. The measured profiles generally agreed well with the standard ridge geometry, indicating good consistency of ridge formation among different sampling locations. After removing the overall terrain variation using a reference trend plane, the standard deviation of the detrended ridge-top elevation residuals was 18.42 mm, indicating relatively small spatial variation in ridge-top elevation. Field observations showed that the formed ridges were continuous and regularly arranged, and soybean emergence after sowing was relatively uniform. The results demonstrated that the developed device could stably construct micro-ridge seedbeds suitable for soybean wide–narrow-row dense planting under Shajiang black soil conditions, providing equipment support for high-quality soybean seedbed preparation in northern Anhui Province.