Medical wastewater is characterized by infectivity, radioactivity, toxicity and drug resistance. In recent years, the total number of medical institutions, the number of beds, and the number of medical practitioners has increased rapidly, and the amount of wastewater discharged has increased dramatically. At present, the medical institution wastewater facilities have played a positive role in sewage pollution control, but there are many problems such as low ownership rate of treatment facilities, low treatment level, poor management and not fully considering ecological andenvironmental safety. This paper takes medical wastewater as the research object, takes big data information as the research background, and uses computer software technology and environmental science technology to complete the development of the interactive system. K-means algorithm isadopted to study the integration of big data interactive system of medical wastewater treatment. Analyze the exceeding degree of medical wastewater treatment to cluster, conduct secondarywastewater treatment, improve the treatment rate of wastewater, and verify the effectiveness of the algorithm through experimental simulation, thus completing the integration study of interactive system.
Key words： Wastewater Treatment, Medical Treatment, The Environment, Big Data, K–means
 Sivarajah, U., Kamal, M.M., Irani, Z., Weerakkody, V. (2017) Critical analysis of Big Data challenges and analytical methods. Journal of Business Research. 70, 263-286.
 Akter, S., Wamba, S.F. (2017) Big data and disaster management: a systematic review and agenda for future research. Annals of Operations Research. 9, 1-21.
 Athey, S. (2017) Beyond prediction: using big data for policy problems. Science. 355, 483-485.
 Xu, W.C., Zhou, H.B., Cheng, N., Lyu, F., Shi, W.S.,Chen, J.Y., Shen, X.M. (2018) Internet of Vehicles inBig Data Era. IEEE/CAA Journal of Automatica Sinica. 5, 19-35.
 Bajari, P., Chernozhukov, V., Hortacsu, A., Hortaçsu, A., Suzuki, J. (2018) The Impact of Big Data on Firm Performance: An Empirical Investigation. Nber Working Papers, National Bureau of Economic Research, Inc.
 Yang, W.X., Li, L.G. (2018) Efficiency evaluation of
industrial waste gas control in China: A study based on data envelopment analysis (DEA) model. Journal ofCleaner Production. 179, 1-11.
 Song, Z.J., Liu, H.M., Meng, F.X., Yuan, X.Y., Feng,Q., Zhou, D.W., Romaní, J.R.V., Yan, H.B. (2019) Zircon U-Pb Ages and Hf Isotopes of Neoproterozoic Meta-Igneous Rocks in the Liansandao Area, NorthernSulu Orogen, Eastern China, and the Tectonic Implications. Journal of Earth Science. 30(6), 1230-1242.
 Choi, E.S., Min, K., Kim, G.J., Kwon, I., Kim, Y.H.(2017) Expression and characterization of Pantoea COdehydrogenase to utilize COcontaining industrial wastegas for expanding the versatility of CO dehydrogenase. Sci Rep. 7, 44323-44324.
 Guo, S.K., Chen, R., Li, H., Zhang, T.L., Liu, Y.Q. (2019) Identify Severity Bug Report with Distribution Imbalance by CR-SMOTE and ELM. International Journal of Software Engineering and Knowledge Engineering. 29, 139-175.