To address the issue of low accuracy and poor robustness in traditional visual odometry methods in weak texture environments, a visual feature-enhanced optical flow-based visual odometry model is proposed. The model first employs a convolutional feature enhancement module that integrates central differential convolution with activation followed by a pooling operation to enhance feature expression and effectively capture local details and edge information. Next, a global local fusion module is implemented to combine detailed and global contextual information, resulting in an aggregated feature representation. Finally, in the offset prediction stage, a convolutional gated linear unit network is used to replace the traditional multilayer perceptron network, thereby enhancing the nonlinear modeling capabilities and improving the accuracy of the auxiliary feature point prediction. The robustness and accuracy of the algorithm are significantly improved by sampling auxiliary features to further enhance the source features. The simulation experiments demonstrate that the proposed model provides more accurate and robust position estimation under weak texture conditions than traditional visual mileage calculation methods.