Conventional digital PCR detection only carries out end-point fluorescence analysis for nucleic acid samples after amplification. Based on fluorescence images obtained after reactions are completed， negative and positive statistics and sample concentrations are calculated. Analysis results are easily affected by false-positives and non-specific amplification. In this paper， a real-time high-throughput digital PCR chip analysis method based on process tracking has been proposed， to quantitatively analyze digital PCR results incorporating the dimension of time， thereby improving the accuracy of digital PCR detection. A system supporting real-time digital PCR analysis was designed and compared with an end-point digital PCR instrument to verify system performance. Using this system， different concentrations of Epstein-Barr virus were detected， and real-time amplification curves were obtained. Support Vector Machine algorithms were used to learn amplification curve characteristics， and applied to classify the detection curves. The amplification results of the designed real-time digital PCR system were highly consistent with that obtained by the end-point digital PCR. The classification algorithm based on Support Vector Machine can achieve more than 98% amplification curve classification accuracy and accurately identify false-positives and non-specific amplification micro-wells. Compared with the traditional threshold segmentation method， the average accuracy of this method for positive recognition is increased by 17.60%. The lower the copy number of the target template， the more obvious this effect is. Compared with traditional end-point digital PCR， data analysis based on the real-time digital PCR system proposed in this paper offers the advantage of higher accuracy. Quantitative result accuracy is especially improved in cases of low copy number detection.