With the explosive growth of Internet information, manual data annotation is time-consuming and subjective, making automated machine-based text recognition and classification crucial due to its high efficiency. As a key method for text annotation, text classification has evolved from human-machine collaboration to full automation, saving computing resources. However, local computers are unable to handle massive volumes of text data. While cloud computing addresses this issue, it faces challenges: the privacy of sensitive data needs protection, and ordinary data is at risk of tampering by third parties. Although encryption algorithms can provide secure solutions, they restrict ciphertext classification and incur high costs, leaving the balance between security, efficiency, and accuracy a major challenge. To tackle this, this paper designs a text classification method under a homomorphic model based on the CKKS homomorphic encryption scheme, and conducts experimental analysis using RNN+Attention as the foundation. The final results demonstrate that the proposed scheme achieves high computational efficiency and accuracy.