ABSTRACT With the fast development of industry, it brings indelible pollution to the natural environment. As a consequence, the air quality is getting worse which will seriously affect people's health. With such concern, continuous air quality monitoring and prediction are necessary. Traditional air quality monitoring methods cannot use large amount of historical data to make accurate predic-tions. Moreover, the traditional prediction method can only roughly predict the air quality level in a short time. With the development of artificial intelligence al-gorithms [1] and high performance computing, the latest mathematical methods and algorithms are able to generate much more accurate predictions based on long term past data. In this master thesis project, it explore to develop deep learning based air quality prediction based on real sensor network time series air quality data from STAIR system [3].