Abstract: We present the system description of our team Zhegu in SemEval-2023 Task 9 Multilingual Tweet Intimacy Analysis. We propose \textbackslash{}textbf\{EPM\} (\textbackslash{}textbf\{E\}xponential \textbackslash{}textbf\{P\}enalty \textbackslash{}textbf\{M\}ean Squared Loss) for the purpose of enhancing the ability of learning difficult samples during the training process. Meanwhile, we also apply several methods (frozen Tuning \textbackslash{}\& contrastive learning based on Language) on the XLM-R multilingual language model for fine-tuning and model ensemble. The results in our experiments provide strong faithful evidence of the effectiveness of our methods. Eventually, we achieved a Pearson score of 0.567 on the test set.