As generalization ability of the out-dated traditional data processing technology is weak, and the technology did not take into account the multi-source data security issues, a blockchain oriented online federated incremental learning algorithm was proposed. Ensemble learning and incremental learning were applied to the framework of federated learning, and stacking ensemble algorithm was used to integrate the local models and the model parameters in model training phase were uploaded to the blockchain with fast synchronization. This made the accuracy of the constructed global model only fall by 1%, while the safety in the stage of training and the stage of storage was improved, so that the costs of the data storage and the transmission of model parameters were reduced, and at the same time, the risk of data leakage caused by model gradient updating was reduced. Experimental results show that the accuracy of the model is over 91.5% and the variance of the model is lower than 10 ^-5, and compared with the traditional integrated data training model, the model has the accuracy slightly reduced, but has the security of data and model improved with the accuracy of the model guaranteed.