Bayesian network is a powerful tool for studying the causal relationship between variables. Causal learning, based on Bayesian network, consists of two parts:structure learning and parameter learning, while structural learning is the core of causal learning. At present, Bayesian network is mainly used to discover the causality in non-time series data (non-time series causality) and what is learned from the data is the causal relationship between general variables. In this study, the causality of time series is learned by time series preconditioning, time series variable sorting, construction of transformation data set, local greedy search-scoring, and so on. Combining the time series preconditioning including segmentation, the structure learning of causal relationship for time series segments, the construction of causality structure data set, the variable sorting of causal relationship, local greedy search-scoring, maximum likelihood parameter estimation, etc., meta causal relationship (used to study the randomness of causal relationship) is established. Thus, two levels of causality learning can be realized, and the foundation is laid for further quantitative causal analysis. Experiments and analyses are carried out by using simulation, UCI, and finance time series, the results verify the validity, reliability, and practicability of learning causal relationship and Meta causality based on Bayesian network.