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This article presents a Hawkes process model with Markovian baseline intensi- ties for high-frequency order book data modeling. We classied intraday order book trading events into a range of categories based on their order types and the price change after their arrivals. In order to capture the stimulating eects between mul- tiple types of order book events, we use multivariate Hawkes process to model the self- and mutually-exciting event arrivals. We also integrate a Markovian baseline intensities into the event arrival dynamic, by including the impacts of order book liquidity state and time factor on the baseline intensity. A regression-based non- parametric estimation procedure is adopted to estimate the model parameters in our Hawkes+Markovian model. To eliminate redundant model parameters, LASSO reg- ularization is incorporated into the estimation procedure. Besides, model selection method based on Akaike Information Criteria is applied to evaluate the eect of each part of the proposed model. An implementation example based on real LOB data is provided. Through the example we studied the empirical shapes of Hawkes excitement functions, the eects of liquidity as well as time factors, the LASSO vari- able selection, and the explanation power of Hawkes and Markovian elements to the dynamics of order book.