The training process of a neural network is the most time-consuming procedure before being deployed to applications. In this paper, we investigate the loss trend of the training data during the training process. We find that given a fixed set of hyper-parameters, pruning specific types of training data can reduce the time consumption of the training process while maintaining the accuracy of the neural network. We developed a data fine-pruning approach, which can monitor and analyse the loss trend of training instances at real-time, and based on the analysis results, temporarily pruned specific instances during the training process basing on the analysis. Furthermore, we formulate the time consumption reduced by applying our data fine-pruning approach. Extensive experiments with different neural networks are conducted to verify the effectiveness of our method. The experimental results show that applying the data fine-pruning approach can reduce the training time by around 14.29% while maintaining the accuracy of the neural network.