To solve the problem wherein motion blur reduces the operational accuracy of visual simultaneous localization and mapping (SLAM), this study proposes an improved visual SLAM method based on blurred image evaluation and feature matching. First, following analysis of the generation principle of image motion blur, a blur parameter is designed based on the re-blurring theory to express the blur degree of the image. Then, adaptive thresholding is used to remove the blurred image. Finally, the grid-based motion statistics algorithm is improved for the feature matching process, replacing the commonly used feature matching method in SLAM. Experiments and analysis of two open source datasets are conducted under different environments. The results show that: 1) the designed blur parameters effectively represent the blur degree of the image. In the prediction of image quality evaluation scores on a standard image library, the root mean square error is reduced by 9.3%-12.3% as compared with other algorithms. Compared with other algorithms when using adaptive thresholds for fuzzy image classification on the KITTI dataset, the accuracy rate and F1 score under the proposed method are increased by 11.0%-17.2% and 22.9%-30.9%, respectively. 2) The improved feature matching algorithm improves the quality of feature matching. Compared with the conventional feature matching algorithm on the KITTI dataset, the interior point rate and matching accuracy under the proposed method are increased by 11.6%-33.1% and 30.4%-38.9%, respectively, and the matching time is reduced by 52.8%-55.8%. 3) In general, the proposed method can reduce the negative effects of motion blur on visual SLAM positioning. Compared with conventional visual SLAM, when processing image sequences of long-distance complex lines, the proposed method reduces the average absolute error and RMS error by 10.4%-26.0% and 10.0%-27.3%, respectively.