Abstract: For the problem of symbolization of time series, the algorithm of immune clustering is adopted to process the symbolization of time series with multi dimension. By using theory of clonal selection, the memory antibody set, which can reflect the real distribution of data, is obtained and used as symbol set. Furthermore, the key problem of decision-making in time-series information system is how to effectively mine the time-order information in history data. Therefore a modified hidden Markov model(HMM) is proposed for decision-making, and the maximum entropy principle is adopted to train the model and calculate probability distribution with maximum entropy. The effectiveness of these methods is proved by an experiment.