Oligonucleotide aptamers are typically identified through a rigorous and time-consuming process known as systematic evolution of ligands by exponential enrichment (SELEX), which requires 20 to 30 iterative rounds to eliminate non/weak binding sequences and enrich tight binding sequences with high affinity. Moreover, inherent experimental biases and non-specific interactions within SELEX could inadvertently exclude high-affinity candidates, leading to a high failure rate. To address these challenges, we proposed DeepAptamer for identifying high-affinity sequences from unenriched early SELEX rounds. As a hybrid neural network model combining convolutional neural networks and bidirectional long short-term memory, DeepAptamer integrated sequence composition and structural features to predict aptamer binding affinities and potential binding motifs. Trained on comprehensive SELEX data, DeepAptamer outperformed existing models in accuracy as substantiated by experimental evidence. More importantly, DeepAptamer effectively identified key nucleotides for target binding. DeepAptamer can efficiently identify high-affinity aptamers against various targets, enhancing its potential to discover promising sequences in initial screening stages and obviating the 20-30 iterative selection rounds required for full enrichment of selection pools. This represented a notable leap forward in aptamer technology, with broad implications for its application across a spectrum of selection targets.