Considering the seasonal， nonlinear and non-stationary characteristics of air passenger demand series， an air passenger demand forecasting model based on a dual decomposition and reconstruction strategy was proposed. Firstly， the air passenger demand series was decomposed twice by Seasonal and Trend decomposition using Loess （STL） and Complete Ensemble Empirical Mode Decomposition with Adaptive Noise （CEEMDAN） methods， and the components were reconstructed based on the feature analysis results of complexity and correlation. Then， Seasonal AutoRegressive Integrated Moving Average （SARIMA）， AutoRegressive Integrated Moving Average （ARIMA）， Kernel based Extreme Learning Machine （KELM） and Bidirectional Long Short-Term Memory （BiLSTM） network models were selected by model matching strategy to predict each reconstructed component respectively， among which the hyperparameters of KELM and BiLSTM models were determined by the Adaptive Tree of Parzen Estimators （ATPE） algorithm. Finally， the prediction results of the reconstruction components were linearly integrated. The air passenger demand data collected from Beijing Capital International Airport， Shenzhen Bao’an International Airport and Haikou Meilan International Airport were taken as research subjects for one-step and multi-step ahead prediction experiments. Experimental results show that compared with the single decomposition ensemble model STL-SAAB， the proposed model has the Root Mean Square Error （RMSE） improved by 14.98% to 60.72%. It can be seen that guided by the idea of “divide and rule”， the proposed model combines model matching and reconstruction strategies to extract the inherent development pattern of the data， which provides a new thinking to scientifically predict the change of air passenger demand.

The traditional graph-based recommendation algorithm neglects the combined time factor which results in the poor recommendation quality. In order to solve this problem, a personalized recommendation algorithm integrating roulette walk and combined time effect was proposed. Based on the user-item bipartite graph, the algorithm introduced attenuation function to quantize combined time factor as association probability of the nodes; Then roulette selection model was utilized to select the next target node according to those associated probability of the nodes skillfully; Finally, the top-N recommendation for each user was provided. The experimental results show that the improved algorithm is better in terms of precision, recall and coverage index, compared with the conventional PersonalRank random-walk algorithm.