To take the spatial distribution into account when identifying and processing single outlier continuous compaction quality, the near neighbor weighted estimation and identification method was developed based on autocorrelation distance. Judge index of single outlier was defined as abnormal index α_i. After eliminating the outliers, the original outlier data were estimated using ordinary Kriging interpolation method. Then continuous compaction experiments were carried out in Loudi construction site of Shanghai-Kunming high-speed railway. Results show that the test value can be determined as a single outlier when the abnormal index α_i is greater than 0.2. Compared with the current Pauta criterion recognition method, the near neighbor weighted estimation and identification method based on autocorrelation distance has higher accuracy and recognition efficiency. The ordinary Kriging interpolation method could provide a more accurate estimation of the single outlier data, reduce the coefficient variation of the data, and improve the uniformity of continuous compaction quality measured value.