Runway visual range (RVR) reflects the pilot’s visual range, which is one of the important meteorological elements to ensure aircraft flight safety when the aircrafts take off and land. Improving the prediction accuracy of RVR will effectively improve the aircraft’s take-off and landing ability and aviation control ability under low visibility and complex weather conditions. The RVR is mainly affected by fog, smoke, dust, heavy precipitation and other weather, as well as the lack of instruments. According to the time series data of meteorological elements such as wind speed, humidity, temperature and runway visibility observed by the civil aviation automated weather observing system of Xianyang Airport from 2012 to 2018, this study firstly analyzes the long-term correlation relationship between the RVR and other meteorological observation data. On the basis of the correlation analysis, this study also uses the long short term memory network (LSTM), which is the most commonly used in artificial intelligence field, to construct an airport RVR prediction model. The experiment results show that the average fitting degree of the model can reach 72% within 0–2 h.