The large volume of unstructured data obtained from Web pages, social media and knowledge bases on the Internet could be represented as an online big graph (OBG). Confronted with many challenges, such as its large-scale, widespread, heterogeneous, and fast-changing properties, OBG data acquisition includes data collection and updating, which is the basis of massive data analysis and knowledge engineering. In this study, the method for adaptive and parallel data collection and updating is proposed based on sampling techniques. First, the HD-QMC algorithm is given for adaptive data collection of OBG data by combining the branch-and-bound method and quasi-Monte Carlo sampling technique. Next, the EPP algorithm is given for efficient data updating based on entropy and Poisson process to make the collected data reflect the dynamic change of OBGs in real-world environments. Further, the effectiveness of the proposed algorithms is analyzed theoretically, and various kinds of collected OBG data are represented by triples universally to provide an easy-to-use data foundation for OBG analysis and relevant studies. Finally, the proposed algorithms for data collection and updating are implemented with Spark, and experimental results on simulated and real-world datasets show the effectiveness and efficiency of the proposed method.