The increasingly crowded and complex airspace environment makes it necessary to determine the initiation of the true target track. Most existing research on radar target track initiation only considers one of real-time or initiation rate, and it is difficult to complete fast and accurate track initiation in a strong clutter environment. In this paper, a track initiation algorithm is proposed based on Deep Learning and Temporal-Spatial(DLTS) characteristics of radar measurement suitable for strong clutter environment. The algorithm first selects the candidate set from the radar measurement combinations, and next extracts the temporal change vector and spatial distribution vector of the measurement combination, and uses them as the input of the One-dimensional Convolutional Neural Network(1DCNN) and Gated Recurrent Unit(GRU) hybrid model to obtain the time dimensional characteristics and space dimensional characteristics of the measurement combination, then merge the two to get the temporal-spatial characteristics. Finally, the true and false tracks are classified with the temporal-spatial characteristics processed by self-attention, and the track initiation is completed. The simulations show that DLTS algorithm can effectively improve the performance of the true and false track initiation rate when the time loss is similar to that of the logic method in the strong clutter environment.