The purpose of this study was to analyze data from consultation records stored at cancer consultation and support centers in Japan as a first step towards systematic utilization of this data. We structured the consultation content of one yearʼs worth of consultation records （9,451 records） using co-occurrence analysis and text-mining methods on consultation items and the text of consultation records in order to clarify patterns and trends in these consultations. As a result, firstly, we were able to visualize combination patterns in the consultation items. Next, based on a diagram of co-occurrence networks in the text of consultation records for each item, we were able to infer the desire to visit a cancer specialized hospital after suspicion or diagnosis of cancer from “examination methods / hospitalization” and the desire to consult with a physician about side effects associated with drug treatment from “response to symptoms / side effects / late effects.” Also, from “medical expenses / living expenses,” we could read mechanisms for reimbursing the high cost of medical expenses, and from “cancer testing” we were able to confirm consultations about how to apply for testing, its costs, water intake before testing, and regular courses of medication. Furthermore, in cases that involved multiple consultation items, it appeared that the patterns of consultation content in each of the relevant items could be handled in combination. By using this method to structure the content of consultation records, the context of the consultation content can be shared with consultation support personnel, thus enabling them to strengthen the knowledge necessary for providing consultations. These findings suggest the usefulness of this approach for handling a large volume of consultation records.