Recently, visual odometry has been widely used in robotics and autonomous driving. Traditional methods for addressing visual odometry are based on complex processes such as feature extraction, feature matching, and camera calibration. Moreover, each module must be integrated to achieve improved results, and the algorithm is high complexity. The interference of environmental noise and the accuracy of the sensor affect the feature extraction accuracy of the traditional algorithm, thereby affecting the estimation accuracy of the visual odometer. In this context, a visual mileage calculation method based on deep learning and fusion attention mechanism is proposed. The proposed method can eliminate the complicated operation process of traditional algorithms. Experimental results show that the proposed algorithm can estimate the camera odometer in real time achieves improved accuracy and stability and reduced network complexity.