Regarding the issues in four-dimensional (4D) millimeter-wave radar simultaneous localization and mapping (SLAM), such as sparse point clouds, instability, and high noise levels, which result in difficulties in point-cloud matching and large cumulative errors, a 4D millimeter-wave radar SLAM algorithm based on local frame fusion is proposed for the 4DRadarSLAM algorithm framework. First, the ego-velocity was estimated to remove noise points and local frame fusion was performed using the pose-transformation relationship of consecutive frames to address sparse point clouds. Subsequently, secondary scan matching was implemented on single and fusion frames to optimize the pose and improve the positioning accuracy of the odometer. Second, the intensity information of radar point clouds was used to construct a scan context descriptor. Combining the average relative error and point-cloud-distribution error to calculate the similarity yields the closed-loop constraint, which effectively reduces the cumulative error. Finally, the odometer factor and closed-loop factor were combined to construct a factor graph to optimize the global pose. Testing and verification results on two types of public datasets from Nanyang Technological University and Shanghai Jiao Tong University show that compared with the 4DRadarSLAM algorithm, the proposed algorithm offers higher accuracy and environmental adaptability, thus providing a new solution for 4D millimeter-wave radar SLAM construction.