Abstract: As one of the vital components of precision lathes, the static stiffness and dynamic characteristics of the lathe bed directly influence the accuracy of the machine tool itself. Owing to the difficulty in establishing a mathematical relationship between the bed parameters and their dynamic characteristics, a research method for the optimal design of the lathe bed based on an improved neural network algorithm was proposed to enhance the static stiffness and dynamic characteristics of the machine tool. Taking a self-developed horizontal CNC lathe of a certain type as an example, Ansys finite element analysis was applied to analyze its natural frequency and static mechanical characteristics. The importance of some bed parameters was evaluated, and these parameters were used as the input of the neural network, thereby obtaining the relationship between the bed parameters, dynamic characteristics, and static stiffness. The optimal range of the bed parameters was further determined through the constraints of the objective function. The research results show that compared with the pre-optimization bed, the weight of the optimized bed increases by 1.65%, the deformation of the guideway surface decreases by 33.33%, the deformation of the bed itself decreases by 21.43%, and the first-order natural frequency increases by 8.33%. These results demonstrate that the research method for the optimal design of the lathe bed based on the neural network algorithm holds significant practical significance for the forward design of solid structures.