In order to solve the problem of fault diagnosis of water injection system in offshore oilfield, a fault diagnosis model based on KShape data augmentation and hybrid neural network was proposed. Firstly, the KShape time series clustering algorithm was used to augment the sample data, then the hybrid neural network model was constructed by fusing bidirectional long short-term memory network (BiLSTM) and fully convolutional neural network (FCN) , and finally the Bayesian optimization algorithm was used to optimize the model parameters globally. The results show the proposed data augmentation method can effectively expand the working condition samples of the water injection system on the basis of digital oilfield big data. The introduced hybrid neural network model outperforms individual BiLSTM and FCN models. This integrated model combines the temporal dependency capture capability of BiLSTM networks with the local feature extraction ability of FCN networks, resulting in a respective improvement of 4.9% and 1.8% in accuracy compared to the two separate models. Bayesian optimization proves significantly effective in discovering optimal hyperparameter combinations, playing a crucial role in enhancing the model's robustness and generalization performance. The method outperforms traditional tuning approaches with a 5% accuracy improvement. Moreover, it surpasses grid search and random search methods by 3.7% and 1.9%, respectively. The model accuracy median under different hyperparameter combinations reaches 84.5%, with an 18% proportion achieving accuracy above 90%. The proposed fault diagnosis model effectively identifies faults such as zone blockage, chock blockage, and wellbore leakage. This model offers a novel solution and technical support for diagnosing faults in offshore oilfield water injection systems.