Decoding emotional states from brain activity has been a long-standing challenge in neuroscience and psychology, with significant implications for understanding human behavior and mental health. In this study, we utilized functional magnetic resonance imaging (fMRI) to decode emotional responses to visual stimuli. The data is from the ICBHI scientific challenge, comprising fMRI scans from multiple participants underwent emotionally tasks with film clips, alongside their subsequent classification into three primary emotional classifications and nine subjective emotional levels. We developed a multi-step analysis pipeline for predicting emotional classification involving initial data filtering,feature selection,and feature extraction through Lasso regression and Kernel Principal Component Analysis(KPCA). Finally, we applied a Random Forest Classifier to predict three types of emotional states,achieving a current model accuracy 65%. Further analysis of the subdivided emotional levels was conducted using a Support Vector Machine(SVM) and a Random Forest Classifier. To address data imbalance, we applied Adaptive Synthetic Sampling (ADASYN), resulting in a model accuracy of 33%. The score calculated by ICBHI scientific challenge is 0.32. This detailed analytical approach demonstrates the utility of these methods in extracting meaningful patterns from complex brain activity data to predict various emotional states. The proposed algorithm has demonstrated robust performance on the challenging and diverse dataset provided by the ICBHI, offering novel insights and a new perspective for the prediction of emotional states