According to phenomenon of missing data in the chemical process, a Locally Weighted Recovery Algorithm (LWRA) for dealing with missing data in the chemical process was proposed based on preserving the local data structure characteristic. The missing data points were located and marked with the symbol NaN (Not a Number), the missing data set was divided into complete data set and incomplete data set. The corresponding k nearest neighbors of incomplete data set were found in the complete data according to the size of integrity in turn, and the corresponding weights of k nearest neighbors were calculated according to the principle of minimum error sum of squares. Finally, the missing data points were reconstructed by k nearest neighbors and their corresponding weights. The algorithm was applied into two types of chemical process data with different missing rates and compared with two traditional data recovery algorithms, Expectation Maximization Principal Component Analysis (EM-PCA) and Mean Algorithm (MA). The results reveal that the proposed method has the lowest error, and the computation speed increases by 2 times in average than EM-PCA. The experimental results demonstrate that the proposed algorithm can not only recover data efficiently but also improve the utilization rate of the data, and it's suitable for nonlinear chemical process data recovery.