Abstract: A time-varying signal reconstruction method based on Reweighted Graph Laplacian Regularization (Reweighted GLR) is proposed to solve the problem that the observed time-varying signals are missing due to noise pollution and machine malfunction, which leads to inaccurate results of the subsequent data processing. algorithm. Firstly, the algorithm constructs a graph model based on the spatial distance information of the data. Secondly, the time-varying graph signal reconstruction problem is summarized as an unconstrained optimization problem based on the spatial domain smoothing property of the time-varying graph signal in the graph model. Finally, the optimization problem is solved by using a reweighted iterative algorithm, which adjusts the edge weights as time changes and dynamically updates the graph Laplacian matrix, such that the inherent correlation of the data as it changes over time is portrayed and the time-space correlation of the time-varying graph signal is fully exploited. Simulation results show that the proposed algorithm further exploits the temporal correlation of time-varying graph signals, reducing reconstruction errors and improving reconstruction performance compared with reconstruction algorithms based on the smoothness of the spatial domain graph of time-varying graph signals.