Transaction data is commonly in various application scenarios, such as shopping records, page browsing history, etc., service providers collect and analyze transaction data for providing better services. However, collecting transaction data will disclose privacy information. To solve the problem, this study proposes a transaction data collection mechanism based on condensed local differential privacy (CLDP). Firstly, a new score function of the candidate set is defined. Secondly, the output domain of the candidate set is separated into several subspaces according to the function. Thirdly, the client selects one subspace randomly, and generates transaction data randomly based on the subspace, then, sends it to the untrusted data collector. Finally, considering the difficulty for setting the privacy parameter, the heuristic privacy parameter setting strategy is designed based on the maximum posterior confidence threat model (MPC). The theoretical analysis shows that this method can protect the length and content of transaction data at the same time and satisfies a-CLDP. The experiments demonstrate that the transaction data collected in this study has higher utility than the state-of-the-art approaches, and the privacy parameter setting is semantic.