{Big data is characterized by large volume, rich types, and rapid growth, but it also has problems such as low value density and poor representativeness, which brings opportunities and challenges to sampling survey. In the context of big data, how does sampling survey adapt to new changes and what kind of development and application does it have? This paper discusses it from three perspectives. First, there are some new sampling methods with strong adaptability in the data stream environment, which can obtain representative samples efficiently and accurately, and take into account the storage space, processing time and ability. Secondly, some non-probability sampling methods without sampling frame have been developed by means of internet survey or social network data collection, which can obtain a large number of analysis samples in a short time at low cost. Third, the advantages of big data and sampling survey are integrated to integrate online and offline survey data. In the case that online sample is non-probability sample and offline sample is probability sample, this article puts forward the basic idea of data integration: On the one hand, probability samples are used to carry out the ``probability test'' for non-probability samples; on the other hand, the information of probability samples is extracted and make inferences based on model or pseudo-randomization.