Background: Peroxisomes are membrane-bound organelles that contain one or more types of oxidative enzymes. Aberrant localization of peroxisomal proteins can contribute to the development of various diseases. To more accurately identify and locate peroxisomal proteins, we developed the ProSE-Pero model. Methods: We employed three methods based on deep representation learning models to extract the characteristics of peroxisomal proteins and compared their performance. Furthermore, we used the SVMSMOTE balanced dataset, SHAP interpretation model, variance analysis (ANOVA), and light gradient boosting machine (LightGBM) to select and compare the extracted features. We also constructed several traditional machine learning methods and four deep learning models to train and test our model on a dataset of 160 peroxisomal proteins using tenfold cross-validation. Results: Our proposed ProSE-Pero model achieves high performance with a specificity (Sp) of 93.37%, a sensitivity (Sn) of 82.41%, an accuracy (Acc) of 95.77%, a Matthews correlation coefficient (MCC) of 0.8241, an F1 score of 0.8996, and an area under the curve (AUC) of 0.9818. Additionally, we extended our method to identify plant vacuole proteins and achieved an accuracy of 91.90% on the independent test set, which is approximately 5% higher than the latest iPVP-DRLF model. Conclusions: Our model surpasses the existing In-Pero model in terms of peroxisomal protein localization and identification. Additionally, our study showcases the proficient performance of the pre-trained multitasking language model ProSE in extracting features from protein sequences. With its established validity and broad generalization, our model holds considerable potential for expanding its application to the localization and identification of proteins in other organelles, such as mitochondria and Golgi proteins, in future investigations.