At present, most of the precipitation nowcasting production is unable to consider the problems of high coverage, high accuracy, and low cost. Therefore, we herein propose a method based on outdoor monitoring images and deep neural network to forecast the rainfall intensity in the next 1 h. We design a dual-flow 3D convolutional neural network to extract high-dimensional features of rainfall information in images. The local information is adaptively generated at a low computational cost, and the temporal and spatial characteristics of rainfall information are extracted by the proposed network which integrates the whole network and the local network using a double loss function. The experimental results show that the neural network based on the dual loss function is better than that based on the single loss function in precipitation intensity forecasting. Percent of doom, false alarm rat, critical success index, and the accuracy of the proposed network are better than those of other models in most cases. In terms of visualization of the model effect, the proposed network can effectively extract the feature information of the precipitation images. Therefore, the proposed precipitation nowcasting method is capable of fine and low-cost precipitation prediction.