Process for generating a database linked to an audiovisual content through different time references, characterised in that it comprises the following steps: - rearrangement of the audiovisual content into sub-scenes, on the basis of the succession of scenes, shots frames, which comprises two parts: - The first part involves a separation of shots, which detects sudden changes in the images, - The second part entails a separation into scenes and identifies those extracted shots that, because they are very similar, may be considered to belong to the same scene, - identification of elements in each of the sub-scenes, which uses, as input elements, the previously rearranged audiovisual content and a database with relevant information automatically obtained from the Internet on the basis of different categories, the following being performed for each of the elements: ∘ a temporal and informational annotation thereof, wherein the temporal annotation indicates the exact moment when each piece of information is identified onscreen and ceases to be visible onscreen, on the basis of the original arrangement of the audiovisual content; ∘ an informational annotation of the different relations (coincidence, content or container, place, belonging, kinship, relation by sharing a third element, etc.) between the different elements, - search for commercial matches in the elements identified, - expert deduplication, annotation and editing, wherein the different categories on the basis of which the elements of each of the sub-scenes are identified are: Men, women, characters, actors, children, creatures and robots, fashion, transport, food and drink, household, technology, sports and leisure, health and beauty, fauna and flora, weapons, art, business, place, time, action, music, references, trivia, miscellanea, quotations, architecture, characterized in that the process for the automatic recognition of actors is divided into two phases: - the first phase is a face detection throughout all the frames in the audiovisual content, by using, a complex algorithm which combines; oriented gradients, in order to locate certain shapes; image pyramids, in order to find those shapes at different scales; spatial sweeping, in order to find them at different places; and a final binary decision-making based on support vector machines; a tracking of the objects detected as faces and a clustering of their trajectories are added, - the second phase entails the specific identification of actors, by training a convolutional neural network model which has a scoring process that is highly parallelisable. - rearrangement of the audiovisual content into sub-scenes, on the basis of the succession of scenes, shots frames, which comprises two parts: - The first part involves a separation of shots, which detects sudden changes in the images, - The second part entails a separation into scenes and identifies those extracted shots that, because they are very similar, may be considered to belong to the same scene, - The first part involves a separation of shots, which detects sudden changes in the images, - The second part entails a separation into scenes and identifies those extracted shots that, because they are very similar, may be considered to belong to the same scene, - identification of elements in each of the sub-scenes, which uses, as input elements, the previously rearranged audiovisual content and a database with relevant information automatically obtained from the Internet on the basis of different categories, the following being performed for each of the elements: ∘ a temporal and informational annotation thereof, wherein the temporal annotation indicates the exact moment when each piece of information is identified onscreen and ceases to be visible onscreen, on the basis of the original arrangement of the audiovisual content; ∘ an informational annotation of the different relations (coincidence, content or container, place, belonging, kinship, relation by sharing a third element, etc.) between the different elements, ∘ a temporal and informational annotation thereof, wherein the temporal annotation indicates the exact moment when each piece of information is identified onscreen and ceases to be visible onscreen, on the basis of the original arrangement of the audiovisual content; ∘ an informational annotation of the different relations (coincidence, content or container, place, belonging, kinship, relation by sharing a third element, etc.) between the different elements, - search for commercial matches in the elements identified, - expert deduplication, annotation and editing, - the first phase is a face detection throughout all the frames in the audiovisual content, by using, a complex algorithm which combines; oriented gradients, in order to locate certain shapes; image pyramids, in order to find those shapes at different scales; spatial sweeping, in order to find them at different places; and a final binary decision-making based on support vector machines; a tracking of the objects detected as faces and a clustering of their trajectories are added, - the second phase entails the specific identification of actors, by training a convolutional neural network model which has a scoring process that is highly parallelisable.