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A data-driven fiber channel modeling method based on deep learning (DL) is introduced in an optical communication system. In this study, bidirectional long short-term memory (BiLSTM) is selected from a diverse range of DL algorithms to perform fiber channel modeling for on–off keying and pulse amplitude modulation 4 signals. Compared with the conventional model-driven split-step Fourier (SSF)-based method, the proposed method yields similar results based on the comprehensive comparison of multiple characteristics associated with the generated optical signals, including the optical amplitude and phase waveforms in the time domain, optical spectrum components in the frequency domain, and eye diagrams after detection in the electrical domain. Additionally, the effects of multiple factors on the modeled fiber channel have also be investigated, including fiber length, fiber nonlinearity, dispersion, data …