Abstract: Construction land is an important factor in the process of the urban development. Prediction of the construction land scale can provide reference data and technical support for general land use planning. This research collected the sociol and economic data from 2004 to 2013 which influenced the construction land scale of Lianyungang, and applied the principal component-BP neural network model to predict the scale of construction land in Lianyungang from 2014 to 2020, then obtained seven years’ prediction results of the scale of construction land in Lianyungang city. The following results can be concluded from the research: (1) The principal component analysis results show that the development of social economy, the changes of population and infrastructure, the improvement of the environment mainly contributed to influencing the scale of Lianyungang construction land from different aspects. (2) The error rate of the BP neural network model built in this research was low and its imitative effect was good, in addition, the model also had good generalization ability to the new sample data from outside of the training sets, which illustrated that the model was credible, so it could be applied to predict the construction land of the future. (3) The construction land scale of Lianyungang showed expansion trend from 2014 to 2020, the average annual growth rate was zero point seven nine percent, the growth rate was relatively fast, which may lead to many social problems. So effective measures should be taken to control the scale of the construction land of Lianyungang and protect arable land legitimately, making the growth of construction land area controlled in a reasonable range. Principal component-BP neural network model can not only comprehensively analyse the influencing factors of the construction land scale, it also can get high precision prediction data of construction land scale. As a result, this model should be well applied to construction land scale prediction.