Identifying posts of high financial quality from opinions is of extraordinary significance for investors. Hence, this paper focuses on evaluating the rationales of amateur investors (ERAI) in a shared task, and we present our solutions. The pairwise comparison task aims at extracting the post that will trigger higher MPP and ML values from pairs of posts. The goal of the unsupervised ranking task is to find the top 10% of posts with higher MPP and ML values. We initially model the shared task as text classification and regression problems. We then propose a multi-learning approach applied by financial domain pre-trained models and multiple linear classifiers for factor combinations to integrate better relationships and information between training data. The official results have proved that our method achieves 48.28% and 52.87% for MPP and ML accuracy on pairwise tasks, 14.02% and -4.17% regarding unsupervised ranking tasks for MPP and ML. Our source code is available.