The Jiangmen Underground Neutrino Observatory (JUNO), currently under construction in the south of China, will be the largest Liquid Scintillator (LS) detector in the world. JUNO is a multipurpose neutrino experiment designed to determine neutrino mass ordering, precisely measure oscillation parameters, and study solar neutrinos, supernova neutrinos, geo-neutrinos and atmospheric neutrinos. The central detector of JUNO contains 20,000 tons of LS and about18,000 20-inch as well as 25,000 3-inch Photomultiplier Tubes (PMTs). The energy resolution is expected to be $3\%/\sqrt{E\mathrm{(MeV)}}$. To meet the requirements of the experiment, two algorithms for the vertex reconstruction have been developed. One is the maximum likelihood method which utilizes the time and charge information of PMTs with good understanding of the complicated optical processes in the LS. The other is the deep learning method with the Convolutional Neural Networks, which is fast and avoids the details of optical processes. In this proceeding, we will present the current status of the two algorithms and their performance will also be discussed based on simulation data.