Abstract: Velocity modeling is a key challenge in geophysical exploration. Although artificial intelligence methods enable efficient model building, they often lack domain-specific guidance and fail to consider the structural similarity between the migration image and velocity model, resulting in inaccurate geological structure interpretation. To address this limitation, we propose a Dix-guided intelligent velocity modeling method, which uses the structural similarity between imaging profiles and interval velocities as well as the Dix relation to optimize the neural network-based modeling process. Specifically, common midpoint(CMP) gathers and velocity spectra are used as inputs to extract features that reflect relative velocity magnitude and variation trends. Then, the stacked profile is introduced to guide the network to convert there features into interval velocities (hidden layers), which are structurally consistent with the profile. This process is constrained by root-mean-square(RMS) velocity lables and a newly designed Dix activation function. Experimental results demonstrate that the Dix activation function effectively guides the network to incorporate geological structures into velocity modeling, enabling it to learn simple morphological structural mappings and enhancing the model's generalization capability. The predicted velocity model accurately delineates stratigraphic boundaries and exhibits strong lateral continuity.