Due to the tight coupling of electric and heating systems, combined heat and power dispatching has become a hot topic. By using the flexibility of heating systems in the aspects of sources, networks and loads, additional space for wind power penetration in the power systems has been provided. However, the available wind power output is difficult to forecast accurately, and its probability distribution cannot be obtained beforehand, while robust dispatch tends to be overly conservative. To address this issue, a data-driven adaptive robust dispatching method is proposed for integrated electric and heat systems. Combining the advantages of stochastic programming and robust optimization, the method simulates worst-case probability distribution scenarios using historical data to achieve a balance between economic efficiency and conservatism of dispatch strategy. The effectiveness of the proposed method is validated through simulation tests on a system consisting of a 6-bus electric network and a 6-node heating network.