Solar radiation flux F_10.7 is widely used in space environment models such as the empirical model of atmosphere and the ionospheric model as an input parameter, and its prediction accuracy directly affects the precision of spacecraft orbit prediction. The relationship between solar radiation flux (F_{10.7) and sunspot number (SSN) is calculated by the time series method, and the linear relationship between them is given. On this basis, we proposed a Short-Term prediction method for F_10.7 in the next 7 days based on the 54-day solar radiation flux index combined with the sunspots number into the LSTM (Long and Short Term Memory) neural network, compared the prediction results with those of other forecasting methods. The results show: (1) The performance of the proposed 7-day method model is better than that of the Space Weather Prediction Center (SWPC), and the correlation coefficient (CC) between the predicted and observed values reaches 0.96. At the same time, the root mean square error is about 11.62 solar radiation flux units, and the RMSE of the forecast result is lower than that of SWPC, which decreases by about 11%. (2) According to the statistical results of 23 and 24 solar activity cycle year, the optimal MAPE (Mean Absolute Percentage Error) of the average absolute percentage error of F_10.7 index on the seventh day of high solar activity year can reach 12.9%, and the optimal MAPE of low solar activity year can reach 2.5%. (3) The results of combined sunspot number LSTM and LSTM using only F_10.7 showed that the newly introduced sunspot number was effective in improving the prediction of LSTM, and the root mean square error (RMSE) of these two models were lower than that of SWPC by about 11% and 5%, respectively.