Machine learning (ML) relies on large amounts of data for training, which often requires outsourcing data to cloud servers. This raises concerns about data integrity and security. Preventing data from being tampered with or lost during storage and transmission has become a critical issue in the ML process. This paper presents a novel data auditing scheme for cloud-based ML, which supports multi-keyword search functionality. It allows searching one or more keywords to ensure the integrity of data in model training. To avoid data privacy leakage, the keywords are encrypted during the auditing process. Only the encrypted keywords of the data required for model training need to be provided to the third-party auditor (TPA). Subsequently, TPA can check and ensure the integrity of the relevant data. Through security analysis and performance evaluations, the results indicate that this scheme can effectively and reliably audit outsourced data while ensuring the security and privacy of model training data.