Current research on automated detection in software engineering for smart contracts primarily focuses on vulnerability identification. Despite early successes in this domain, a more critical concern is the developers’ intent behind smart contracts, as those with malicious intent have led to significant financial losses. Unfortunately, existing research lacks effective methods for detecting the intent in smart contracts. To address this challenge, we propose a deep learning model, SMARTINTENTNN (Smart Contract Intent Neural Network), for the automated detection of smart contract development intent. SMARTINTENTNN utilizes a pre-trained sentence encoder to generate contextual representations of smart contract code, a K-means clustering model to identify and highlight prominent intent features, and a bidirectional LSTM-based deep neural network for multi-label classification. We trained and evaluated SMARTINTENTNN on a dataset of over 40,000 real-world smart contracts, using self-comparison baselines in our experimental setup. The results demonstrate that SMARTINTENTNN achieves an F1-score of 0.8633 in identifying intents across 10 distinct categories, outperforming all baselines and addressing the gap in smart contract detection by incorporating intent analysis.