Abstract: This research systematically investigated the challenges and opportunities brought by the big data explosion to the traditional research paradigms in the context of the fourth industrial revolution. Taking the predictive model of livability for Dutch smart cities, which had been developed through machine learning, as an example, the research introduced “the fourth paradigm” as a novel data-intensive research method. The research discussed the traditional research paradigms and the status of livability research, collected applicable variables and available multi-source big data sets related to the livability of human settlements. Afterward, the research proceeded with necessary data cleansing, data engineering, and feature engineering to ensure that the collected raw data sets met the basic requirements of machine learning. In the subsequent machine learning experiments, the research selected two general algorithms, namely Multiclass Decision Jungle and Multiclass Decision Forest, to carry out a multiclass prediction in supervised machine learning. These were compared and optimized to obtain an algorithm with higher accuracy. This optimized algorithm was deployed to the cloud to produce a smart forecasting toolbox to monitor, predict, and perform an early intervention on the livability of the Dutch environment. The research demonstrated that cutting-edge AI algorithms and emerging machine learning technologies developed on the basis of “the fourth paradigm” have a pronounced advantage in knowledge discovery, quantitative analysis, rapid knowledge update, and predictive research, as compared with the traditional research paradigms. The proposed paradigm is more efficient, innovative, and capable of dealing with research containing data sets with large volume, more variety, veracity, and velocity for future smart cities.