Method for automatically analyzing at least one transaction log of a distributed computer system, said log including a plurality of lines, the method including the following steps: - for each line of the transaction log: ∘ cutting the line into words, each word being associated with a position on the line, and associating the line with a size that is defined as the number of words; ∘ replacing each word with a descriptor; ∘ constructing a comparison vector by comparing the line with the other lines of the transaction log associated with the same size as said line, the comparison vector comprising, for each word, an occurrence corresponding to the number of times the descriptor corresponding to the word appears, at the position associated with the word, in another line of the transaction log associated with the same size as the line (201, 103); ∘ constructing a pattern from at least one word of the line from the comparison vector, the pattern comprising each descriptor of the line associated with an occurrence appearing a maximum number of times in the comparison vector; - creating an event per pattern, each event being associated with the number of lines having, for a pattern, the pattern corresponding to the event,; - training, in an unsupervised manner, an artificial neural network on a group (400_1) of training events including at least one event, corresponding to a set (202_1) of training lines comprising a plurality of lines of the transaction log, to predict the next event that will take place in the transaction log (200, 107); - for at least one event: ∘ using the artificial neural network to predict the event as the next event that will take place in the transaction log, from a group (400_2) of prediction events comprising at least one event having occurred before the event in the transaction log (200, 108); ∘ generating, from the artificial neural network, a causality graph of the event comprising a causal relationship for each event of the group (400_2) of prediction events meeting a relevance condition. - for each line of the transaction log: ∘ cutting the line into words, each word being associated with a position on the line, and associating the line with a size that is defined as the number of words; ∘ replacing each word with a descriptor; ∘ constructing a comparison vector by comparing the line with the other lines of the transaction log associated with the same size as said line, the comparison vector comprising, for each word, an occurrence corresponding to the number of times the descriptor corresponding to the word appears, at the position associated with the word, in another line of the transaction log associated with the same size as the line (201, 103); ∘ constructing a pattern from at least one word of the line from the comparison vector, the pattern comprising each descriptor of the line associated with an occurrence appearing a maximum number of times in the comparison vector; ∘ cutting the line into words, each word being associated with a position on the line, and associating the line with a size that is defined as the number of words; ∘ replacing each word with a descriptor; ∘ constructing a comparison vector by comparing the line with the other lines of the transaction log associated with the same size as said line, the comparison vector comprising, for each word, an occurrence corresponding to the number of times the descriptor corresponding to the word appears, at the position associated with the word, in another line of the transaction log associated with the same size as the line (201, 103); ∘ constructing a pattern from at least one word of the line from the comparison vector, the pattern comprising each descriptor of the line associated with an occurrence appearing a maximum number of times in the comparison vector; - creating an event per pattern, each event being associated with the number of lines having, for a pattern, the pattern corresponding to the event,; - training, in an unsupervised manner, an artificial neural network on a group (400_1) of training events including at least one event, corresponding to a set (202_1) of training lines comprising a plurality of lines of the transaction log, to predict the next event that will take place in the transaction log (200, 107); - for at least one event: ∘ using the artificial neural network to predict the event as the next event that will take place in the transaction log, from a group (400_2) of prediction events comprising at least one event having occurred before the event in the transaction log (200, 108); ∘ generating, from the artificial neural network, a causality graph of the event comprising a causal relationship for each event of the group (400_2) of prediction events meeting a relevance condition. ∘ using the artificial neural network to predict the event as the next event that will take place in the transaction log, from a group (400_2) of prediction events comprising at least one event having occurred before the event in the transaction log (200, 108); ∘ generating, from the artificial neural network, a causality graph of the event comprising a causal relationship for each event of the group (400_2) of prediction events meeting a relevance condition.