Functional magnetic resonance imaging (fMRI) allows for the temporal observation of human brain activity, providing insights into the activation patterns of various brain regions under different emotional states. Given the complexity of brain data, influenced by numerous factors, accurately predicting emotions using fMRI data presents a significant challenge. This study aims to develop an advanced artificial intelligence model for emotion recognition based on a comprehensive set of fMRI datasets. The dataset includes fMRI data from 20 participants engaged in emotion-inducing tasks, such as watching video clips, with emotional responses categorized into three classes and nine levels. The fMRI signals were segmented into 25-s intervals, capturing neural activity across 246 brain regions. To enhance signal quality and reduce noise, we employed a nonlinear least squares method, where each 25-s signal segment was modeled using a polynomial function. The polynomial parameters were optimized to minimize the sum of squared differences between observed and predicted values. Initially, a fully connected neural network (FCNN) model was trained to identify the brain regions whose activity patterns are most strongly correlated with emotional states. These identified regions were then selected for further training. Subsequently, the signals from the selected brain regions are concatenated for training multiple Fully Convolutional Neural Network (FCNN) models. An ensemble voting system is then employed to predict the unknown dataset, with the most frequently occurring prediction result being used as the final output. Our emotion recognition system achieved an accuracy of approximately 52–58% for class predictions and around 30% for level predictions. The model’s performance is further evidenced by a submission score of 0.5704. These findings underscore the strong association between brain fMRI data and emotions, demonstrating the potential of FCNNs in decoding emotional states from brain fMRI data. Despite challenges such as data heterogeneity and individual variability, the model’s evaluation on the ICBHI dataset indicates significant predictive capabilities, contributing to our understanding of the neural mechanisms of emotions.