Clustering white matter streamlines is still a challenging task. The existing methods based on spatial coordinates rely on manually engineered features, and/or labeled dataset. This work introduced a novel method that solves the problem of streamline clustering without needing labeled data. This is achieved by training a deep LSTM-based autoencoder to learn and embed any lengths of streamlines into a fixed-length vector, i.e. latent representation, then perform clustering in an unsupervised learning manner.