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As an essential element in the overall designing of a service system, service planning plays an important role in improving final service quality and user experience. Service planning is a customer-oriented approach that facilitates the translation of customer requirements into activity process features or engineering characteristics. The trend is towards learning decision-support knowledge from massive service case data to facilitate service planning. However, when learning from imbalanced data, existing methods have poor predictive ability to identify minority patterns. To improve the quality and efficiency of the service planning, an innovative approach based on the combination of granular computing and deep learning are presented. It employs an inductive paradigm, clustering examples in a best granularity, sampling and refining into a more balanced example set, and feeding them into a deep learning model. The proposed approach can mine the planning patterns between the customer requirements and service process features, thereby facilitating knowledge transferring in service planning under the social manufacturing context. [ABSTRACT FROM AUTHOR]