Abstract: Implicit sentiment analysis plays a crucial role in understanding latent emotional expressions embedded in textual content. However, most existing sentiment analysis methods primarily focus on explicit sentiment cues while neglecting implicit sentiment information commonly found in real-world user-generated texts. This challenge is particularly pronounced in Chinese texts, where rich semantic structures and rhetorical devices, such as metaphors and similes, frequently obscure underlying sentiment expressions. To address these issues, a novel implicit sentiment analysis framework, termed CKECL, is proposed. First, a pre-trained translation model is employed to generate cross-lingual knowledge for data augmentation. The generated cross-lingual information enriches semantic representations and improves the model's capability to capture implicit sentiment patterns. Second, a bidirectional long short-term memory (BiLSTM) network is adopted to model contextual dependencies from both forward and backward directions, thereby enhancing sentence-level semantic representation. Finally, information from sentences, is used to better capture contextual features within the sentences. Finally, contrastive learning is introduced to learn more discriminative sentence representations by integrating cross-lingual knowledge with contextual features. Experimental results on multiple benchmark datasets demonstrate that the proposed approach consistently outperforms competing baselines, confirming its effectiveness.