With the application and comprehensive development of big data, the need for effective research on cloud workflow management and scheduling is becoming more and more urgent. However, there are currently suitable methods for effective analysis. In order to find out how to effectively manage and schedule smart cloud workflows, the article studies big data from different aspects and draws the following conclusions: Compared with the original JStorm system, the average response time is shortened by up to 58.26%, and the average is shortened. 23.18%; CPU resource utilization increased by 17.96%, an average increase of 11.39%; memory utilization increased by 88.7%, an average increase of 71.16%. In optimizing the dynamic combination of web services, the overall performance of MOACO algorithm and CCA algorithm is better than GA algorithm, and the average performance of MOACO algorithm is better than CCA algorithm. The paper also proposes a cloud workflow scheduling strategy based on intelligent algorithms and adjusting the perceived cloud service resource combination strategy to realize two-layer scheduling of cloud workflow tasks. We have studied three representative intelligent algorithms (ACO algorithm, PSO algorithm and GA algorithm) and designed and improved them for scheduling optimization. It can be clearly seen that in the same scenario, in different test cases the optimal values of the different algorithms vary greatly. However, the optimal solution curve is substantially consistent with the trend of the mean curve.