Traditional time series forecasting models perform poorly in forcasting the time series data with long-term and short-term time relevance, nonlinearity and non-stationarity. To improve the accuracy and efficiency of the time series forecasting model, this study proposed a time series forecasting model (Attention Temporal Convolutional Neural Network, A-TCNN) combining temporal convolution, residual structure, and the attention mechanism. The model considers the efficiency of temporal convolution to extract temporal features, the superiority of residual structure to accelerate model convergence, and the strengthening effect of the attention mechanism on the parameters. Firstly, the long-term and short-term features are extracted from the data through multiple residual temporal convolutional layers; secondly, the weight of the parameters that have a greater impact on the output is strengthened through the attention layers; finally, the output result is obtained through a fully connected layer. On the dataset of actual hospital finance, a variety of multi-step prediction strategies are compared with those in conventional networks. The experimental results show that this model has higher prediction accuracy and efficiency compared with conventional models.