Stochastic configuration networks (SCNs) have been successfully applied to big data analysis with their general approximation capability and fast modeling properties. Based on the SCNs, stochastic configuration networks with block increments (BSC) use block increment mechanism to improve the training speed, but increase the complexity of model structure. To solve the above challenges, an adaptive switching learning model based on forgetting factor stochastic configuration networks (FSCN-I and FSCN-II) with (ASLM) is proposed. FSCN-I adjusts the size of node blocks by error values and forgetting factors to improve the training speed, and FSCN-II introduces a node removal mechanism to reduce the complexity of the model structure. ASLM consists of FSCN-I and FSCN-II, both of which are randomly switched according to the adaptively changing boundaries to improve the training speed of the model and the complexity of the model structure is reduced based on FSCN-I. Finally, the effectiveness of the method is demonstrated with the underlying dataset and industrial examples.