To study the influence of market characteristics on stock prices, traditional neural network algorithm may also fail to predict the stock market precisely, since the initial weight of the random selection problem can be easily prone to incorrect predictions. Based on the idea of word vector in deep learning, we demonstrate the concept of stock vector. The input is no longer a single index or single stock index, but multi-stock high-dimensional historical data. We propose the deep long-short term memory neural network (LSMN) with embedded layer to predict the stock market. In this model, we use the embedded layer to vectorize the data, in a bid to forecast the stock via long-short term memory neural network. The experimental results show that the deep long short term memory neural network with embedded layer is state-of-the-art in developing countries. Specifically, the accuracy of this model is 57.2% for the Shanghai A-shares composite index. Furthermore, this is 52.4% for individual stocks.