Under the background of multi-value chains collaborative development, the impact of other value chains such as service chain and marketing chain on product demand has not been fully considered by manufacturing enterprises. In order to improve the accuracy of product demand forecasting in manufacturing enterprises, a product data space and a deep learning algorithm of one-dimensional convolutional neural networks (1D-CNN) - long short-term memory (LSTM) were proposed. Firstly, the relevant data of the impact of different value chains on product demand were integrated to build a product data space. Secondly, the multi-value chain data set was obtained from the data space for the prediction of 1D-CNN-LSTM model. Among them, the deep-seated features of the data were obtained through two convolution and pooling operations of 1D-CNN. And the next step of time series prediction was realized by furthering learning the important information in the data deep-seated features through LSTM. Finally, an example was analyzed through the relevant data of ring main units by an electrical equipment manufacturing enterprise, and the prediction results were compared with other models. The results show that the prediction effect of 1D-CNN-LSTM model is better than that of neural network models and a single LSTM model. It is concluded that the 1D-CNN-LSTM deep learning model proposed in this paper has more advantages and good prediction effect.