A parallel Bayesian optimization method based on the adaptive surrogate model is proposed to solve the computationally expensive problems. Based on the multi-points expected improvement criterion, one can evaluate the batch of points simultaneously. For the problem that the global surrogate model is difficult to be constructed with a large number of historical data from the parallel optimization, an improved data parallel local learning methodology for Gaussian Process modeling is proposed to construct the local surrogate models online. Furthermore, in order to reduce the computational cost of the multi-points expected improvement criterion, a heuristic hierarchical optimization strategy is proposed to calculate the single point expected improvement criterion based on the adaptive surrogate model sequentially. Finally, the effectiveness of the proposed method is verified by 5 test problems.