Multiple time series land using spatial-temporal evolution analysis is an important research area. In this study, we investigated the spatial-temporal integrated expression of multiple time series land use change. A self-organizing map (SOM) neural network was used to explore regional land use change modes and to analyze what has driven these changes. Remote sensing data for five land use classification data periods (2005, 2007, 2009, 2011, and 2013) for Beijing were used to train the network, and the outputs identified the aggregation modes for building land, farmland, forest land, grassland, and gardens by using the clustering, dimension-reducing, and visual functions of the SOM. Then we conducted second-step clustering to produce the neuron and build the land use change trajectories that are needed to analyze the spatial-temporal features of Beijing suburban land use changes during the five monitoring periods. The results revealed that there were two land use changes in the Beijing suburbs between 2005 and 2013. One was the development of buildings on farmland located on the plains and the other was the development of forest land in mountainous areas. Furthermore, development in each district had its own time sequences. This meant that we eventually obtained six land use change trajectories in total.