Hydraulic fracturing is one of the main ways to increase oil and gas production. However, with existing methods, the diameter of the nozzle cannot be easily adjusted. This therefore results in ‘sand production’ in flowback fluid, affecting the application of hydraulic fracturing. This is because it is difficult to identify the one-dimensional series signal of fracturing fluid collected on site. In order to avoid ‘sand production’ in the flowback fluid, the nozzle should be properly controlled. Aiming to address this problem, a novel augmented residual deep learning neural network (AU-RES) is proposed that can identify the characteristics of multiple one-dimensional time series signals and effectively predict the diameter of the nozzle. The AU-RES network includes three parts: signal conversion layer, residual and convolutional layer, fully connected layer (including regression layer). Firstly, a spatial conversion algorithm for multiple one-dimensional time series signals is proposed, which can transform the one-dimensional time series signals into images in high dimensional space. Secondly, the features of the images are extracted and identified by the residual network. Thirdly, the network hyperparameters are optimized to improve the prediction accuracy of the network. Simulations and experiments performed on the field data samples show that the RMSE and LOSS when training the AU-RES network are 0.131 and 0.00021, respectively, and the prediction error of the test samples is 0.1689. In the gas field experiments, fracturing fluid sand production could be controlled, thus demonstrating the validity and reliability of the AU-RES network. By using the AU-RES neural network, sand particles will not be present in the flowback of fracturing fluid, thus improving the efficiency of hydraulic fracturing and reducing the cost of hydraulic fracturing. In addition, the AU-RES network can also be used in other similar situations.