In order to find the change laws of human meridian and to prove the laws' consistency with Traditional Chinese Medicine theory, conductance series data of 72 acupoints from 10 volunteers was collected for 2 years. Visualized analysis method is used in this paper to find the laws, as it a good way to find change laws before there's a definite research target. As it is a tough job to collect data form two years, this data is incomplete and has missing values. Traditionally, researches have to remove the incomplete samples. In this article, we put forward a novel method which estimates missing values in meridian dataset with Bayesian principal component analysis (BPCA) algorithm first and then visualize these values. With the proposed method, some useful characteristics of meridian conductance data were found.