To address the challenges of high volatility and significant output uncertainty of renewable energy sources in the planning of hydrogen-containing integrated energy systems, a robust optimal scheduling method considering source-load uncertainty with electricity-heat-cooling-gas multi-energy synergy was proposed. Firstly, an initial scenario set was constructed utilizing the Latin Hypercube Sampling technique, and redundant scenarios were reduced by combining with an improved K-means algorithm to achieve efficient characterization of uncertainty. Secondly, the structural characteristics of the systems were analyzed in depth, and a multi-energy flow coupling model encompassing electricity, heat, cooling, gas, and hydrogen were established. Subsequently, a robust optimization approach were adopted to transform source-load uncertainty into a function of predictor variables, and the uncertainty set were modeled using the envelope boundary method. Finally, a multi-objective optimization model were constructed aiming at the minimum total system cost and optimal carbon emission reduction. Case studies verify that the proposed the systems model can significantly improve low-carbon benefits while guaranteeing economic operation, and accurately match the diversified energy demands of users, thereby validating the effectiveness and practicality of the method.